Despite the lack of invariance problem (the many‐to‐many mapping between acoustics and percepts), human listeners experience phonetic constancy and typically perceive what a speaker intends. Most models of human speech recognition (HSR) have side‐stepped this problem, working with abstract, idealized inputs and deferring the challenge of working with real speech. In contrast, carefully engineered deep learning networks allow robust, real‐world automatic speech recognition (ASR). However, the complexities of deep learning architectures and training regimens (...) make it difficult to use them to provide direct insights into mechanisms that may support HSR. In this brief article, we report preliminary results from a two‐layer network that borrows one element from ASR, long short‐term memory nodes, which provide dynamic memory for a range of temporal spans. This allows the model to learn to map real speech from multiple talkers to semantic targets with high accuracy, with human‐like timecourse of lexical access and phonological competition. Internal representations emerge that resemble phonetically organized responses in human superior temporal gyrus, suggesting that the model develops a distributed phonological code despite no explicit training on phonetic or phonemic targets. The ability to work with real speech is a major advance for cognitive models of HSR. (shrink)

It is well known that the unpredictable speech production brought on by stress from the task at hand has a significant negative impact on the performance of speech processing algorithms. Speech therapy benefits from being able to detect stress in speech. Speech processing performance suffers noticeably when perceptually produced stress causes variations in speech production. Using the acoustic speech signal to objectively characterize speaker stress is one method for assessing production variances brought on (...) by stress. Real-world complexity and ambiguity make it difficult for decision-makers to express their conclusions with clarity in their speech. In particular, the Neutrosophic speech algorithm is used to encode the language variables because they cannot be computed directly. Neutrosophic sets are used to manage indeterminacy in a practical situation. Existing algorithms are used except for stress on Neutrosophic speech recognition. The creation of algorithms that calculate, categorize, or differentiate between different stress circumstances. Understanding stress and developing strategies to combat its effects on speech recognition and human-computer interaction system are the goals of this recognition. (shrink)

Speech recognition technology has played an indispensable role in realizing human-computer intelligent interaction. However, most of the current Chinese speech recognition systems are provided online or offline models with low accuracy and poor performance. To improve the performance of offline Chinese speech recognition, we propose a hybrid acoustic model of deep convolutional neural network, long short-term memory, and deep neural network. This model utilizes DCNN to reduce frequency variation and adds a batch normalization (...) layer after its convolutional layer to ensure the stability of data distribution, and then use LSTM to effectively solve the gradient vanishing problem. Finally, the fully connected structure of DNN is utilized to efficiently map the input features into a separable space, which is helpful for data classification. Therefore, leveraging the strengths of DCNN, LSTM, and DNN by combining them into a unified architecture can effectively improve speech recognition performance. Our model was tested on the open Chinese speech database THCHS-30 released by the Center for Speech and Language Technology of Tsinghua University, and it was concluded that the DLD model with 3 layers of LSTM and 3 layers of DNN had the best performance, reaching 13.49% of words error rate. (shrink)

The accurate recognition of speech is beneficial to the fields of machine translation and intelligent human–computer interaction. After briefly introducing speech recognition algorithms, this study proposed to recognize speech with a recurrent neural network (RNN) and adopted the connectionist temporal classification (CTC) algorithm to align input speech sequences and output text sequences forcibly. Simulation experiments compared the RNN-CTC algorithm with the Gaussian mixture model–hidden Markov model and convolutional neural network-CTC algorithms. The results demonstrated (...) that the more training samples the speech recognition algorithm had, the higher the recognition accuracy of the trained algorithm was, but the training time consumption increased gradually; the more samples a trained speech recognition algorithm had to test, the lower the recognition accuracy and the longer the testing time. The proposed RNN-CTC speech recognition algorithm always had the highest accuracy and the lowest training and testing time among the three algorithms when the number of training and testing samples was the same. (shrink)

For many women and girls sexist and misogynistic language is an everyday experience. Some instances of this speech can be categorized as ‘sexist hate speech’, as not only having an insulting or degrading character towards the individuals to whom the speech is addressed, but also resonating with the entire group, contributing to its silencing, marginalization and exclusion. The aim of this article is to examine how sexist hate speech is handled in international human rights law. (...) The argument derives from the claim than that legal discourse should distinguish between ‘sexist speech’ and ‘sexist hate speech’, and that the later may be qualified as a form of violence against women. Then it analyses the approaches of two human rights protection systems—the United Nations and the Council of Europe system—towards the phenomenon of sexist hate speech, by taking their position towards hate speech in general as a point of reference. In both systems, sexist hate speech is being addressed more eagerly under the gender equality framework, including counteracting violence against women, than in their interpretation of hate speech under general human rights law. The article argues the importance of recognizing and addressing sexist hate speech both within the framework of gender equality instruments and anti-hate speech framework established by international human rights bodies. (shrink)

Although researchers studying human speech recognition (HSR) and automatic speech recognition (ASR) share a common interest in how information processing systems (human or machine) recognize spoken language, there is little communication between the two disciplines. We suggest that this lack of communication follows largely from the fact that research in these related fields has focused on the mechanics of how speech can be recognized. In Marr's (1982) terms, emphasis has been on the algorithmic (...) and implementational levels rather than on the computational level. In this article, we provide a computational-level analysis of the task of speech recognition, which reveals the close parallels between research concerned with HSR and ASR. We illustrate this relation by presenting a new computational model of human spoken-word recognition, built using techniques from the field of ASR that, in contrast to current existing models of HSR, recognizes words from real speech input. (shrink)

Although researchers studying human speech recognition (HSR) and automatic speech recognition (ASR) share a common interest in how information processing systems (human or machine) recognize spoken language, there is little communication between the two disciplines. We suggest that this lack of communication follows largely from the fact that research in these related fields has focused on the mechanics of how speech can be recognized. In Marr's (1982) terms, emphasis has been on the algorithmic (...) and implementational levels rather than on the computational level. In this article, we provide a computational-level analysis of the task of speech recognition, which reveals the close parallels between research concerned with HSR and ASR. We illustrate this relation by presenting a new computational model of human spoken-word recognition, built using techniques from the field of ASR that, in contrast to current existing models of HSR, recognizes words from real speech input. (shrink)

Intentionalism is a research program that seeks to explain facts about meaning and communication in psychological terms, with our capacity for intention recognition playing a starring role. My aim here is to recommend a methodological reorientation in this program. Instead of a focus on intuitive counterexamples to proposals about necessary-and-sufficient conditions, we should aim to investigate the psychological mechanisms whose activities and interactions explain our capacity to communicate. Taking this methodologi- cal reorientation to heart, I sketch a theory of (...) the cognitive architecture underlying language use that I have defended elsewhere. I then show how this theory can be used to give an account of non-communicative language use—a phenomenon that has long posed a challenge to intentionalism. (shrink)

Successful language use requires accurate intention recognition. However, sometimes this can be undermined because communication occurs within an interpersonal context. In this research, I used a relatively large set of speech acts (n = 32) and explored how variability in their inherent face‐threat influences the extent to which they are successfully recognized by a recipient, as well as the confidence of senders and receivers in their communicative success. Participants in two experiments either created text messages (senders) designed to (...) perform a specific speech act (e.g., agree) or interpreted those text messages (receivers) in terms of the specific speech act being performed. The speech acts were scaled in terms of their degree of face threat. In both experiments, speech acts that were more threatening were less likely to be correctly recognized than those that were less threatening. Additionally, the messages of the more threatening speech acts were longer and lower in clout than the less threatening speech acts. Senders displayed greater confidence in communicative success than receivers, but judgments of communicative success (for both senders and receivers) were unrelated to actual communicative success. The implications of these results for our understanding of actual communicative episodes are discussed. (shrink)

The article analyzes the neural and functional grounding of language skills as well as their emergence in hominid evolution, hypothesizing stages leading from abilities known to exist in monkeys and apes and presumed to exist in our hominid ancestors right through to modern spoken and signed languages. The starting point is the observation that both premotor area F5 in monkeys and Broca's area in humans contain a “mirror system” active for both execution and observation of manual actions, and that F5 (...) and Broca's area are homologous brain regions. This grounded the mirror system hypothesis of Rizzolatti and Arbib (1998) which offers the mirror system for grasping as a key neural “missing link” between the abilities of our nonhuman ancestors of 20 million years ago and modern human language, with manual gestures rather than a system for vocal communication providing the initial seed for this evolutionary process. The present article, however, goes “beyond the mirror” to offer hypotheses on evolutionary changes within and outside the mirror systems which may have occurred to equip Homo sapiens with a language-ready brain. Crucial to the early stages of this progression is the mirror system for grasping and its extension to permit imitation. Imitation is seen as evolving via a so-called simple system such as that found in chimpanzees (which allows imitation of complex “object-oriented” sequences but only as the result of extensive practice) to a so-called complex system found in humans (which allows rapid imitation even of complex sequences, under appropriate conditions) which supports pantomime. This is hypothesized to have provided the substrate for the development of protosign, a combinatorially open repertoire of manual gestures, which then provides the scaffolding for the emergence of protospeech (which thus owes little to nonhuman vocalizations), with protosign and protospeech then developing in an expanding spiral. It is argued that these stages involve biological evolution of both brain and body. By contrast, it is argued that the progression from protosign and protospeech to languages with full-blown syntax and compositional semantics was a historical phenomenon in the development of Homo sapiens, involving few if any further biological changes. Key Words: gestures; hominids; language evolution; mirror system; neurolinguistics; primates; protolanguage; sign language; speech; vocalization. (shrink)

Speech recognition plays an important role in human–computer interaction. The higher the accuracy and efficiency of speech recognition are, the larger the improvement of human–computer interaction performance. This article briefly introduced the hidden Markov model -based English speech recognition algorithm and combined it with a back-propagation neural network to further improve the recognition accuracy and reduce the recognition time of English speech. Then, the BPNN-combined HMM algorithm was simulated and (...) compared with the HMM algorithm and the BPNN algorithm. The results showed that increasing the number of test samples increased the word error rate and recognition time of the three speech recognition algorithms, among which the word error rate and recognition time of the BPNN-combined HMM algorithm were the lowest. In conclusion, the BPNN-combined HMM can effectively recognize English speeches, which provides a valid reference for intelligent recognition of English speeches by computers. (shrink)

In the process of communication between people, everyone will have emotions, and different emotions will have different effects on communication. With the help of external performance information accompanied by emotional expression, such as emotional speech signals or facial expressions, people can easily communicate with each other and understand each other. Emotion recognition is an important network of affective computers and research centers for signal processing, pattern detection, artificial intelligence, and human-computer interaction. Emotions convey important information in (...) class='Hi'>human communication and communication. Since the end of the last century, people have started the research on emotion recognition, especially how to correctly judge the emotion type has invested a lot of time and energy. In this paper, multi-modal emotion recognition is introduced to recognize facial expressions and speech, and conduct research on adaptive higher education management. Language and expression are the most direct ways for people to express their emotions. After obtaining the framework of the dual-modal emotion recognition system, the BOW model is used to identify the characteristic movement of local areas or key points. The recognition rates of emotion recognition for 1,000 audios of anger, disgust, fear, happiness, sadness and surprise are: 97.3, 83.75, 64.87, 89.87, 84.12, and 86.68%, respectively. (shrink)

This paper outlines the background development of “intelligent“ technologies such as speech recognition. Despite significant progress in the development of these technologies, they still fall short in many areas, and rapid advances in areas such as dictation are actually stalled. In this paper we have proposed semi-automatic solutions — smart integration of human and intelligent efforts. One such technique involves improvement to the speech recognition editing interface, thereby reducing the perception of errors to the viewer. (...) Other techniques that are described in the paper are batch enrollment, which allows the user to reduce the amount of time required for enrollment, and content spotting, which can be used for applications that have repeated content flow, such as movies or museum tours. The paper also suggests a general concept of distributive training of speech recognition systems that is based on data collection across a network. (shrink)

Action perception and recognition are core abilities fundamental for human social interaction. A parieto-frontal network (the mirror neuron system) matches visually presented biological motion information onto observers' motor representations. This process of matching the actions of others onto our own sensorimotor repertoire is thought to be important for action recognition, providing a non-mediated “motor perception” based on a bidirectional flow of information along the mirror parieto-frontal circuits. State-of-the-art machine learning strategies for hand action identification have shown better (...) performances when sensorimotor data, as opposed to visual information only, are available during learning. As speech is a particular type of action (with acoustic targets), it is expected to activate a mirror neuron mechanism. Indeed, in speech perception, motor centers have been shown to be causally involved in the discrimination of speech sounds. In this paper, we review recent neurophysiological and machine learning-based studies showing (a) the specific contribution of the motor system to speech perception and (b) that automatic phone recognition is significantly improved when motor data are used during training of classifiers (as opposed to learning from purely auditory data). (shrink)

We assessed how synchronous speech listening and lipreading affects speech recognition in acoustic noise. In simple audiovisual perceptual tasks, inverse effectiveness is often observed, which holds that the weaker the unimodal stimuli, or the poorer their signal-to-noise ratio, the stronger the audiovisual benefit. So far, however, inverse effectiveness has not been demonstrated for complex audiovisual speech stimuli. Here we assess whether this multisensory integration effect can also be observed for the recognizability of spoken words. To that (...) end, we presented audiovisual sentences to 18 native-Dutch normal-hearing participants, who had to identify the spoken words from a finite list. Speech-recognition performance was determined for auditory-only, visual-only (lipreading) and auditory-visual conditions. To modulate acoustic task difficulty, we systematically varied the auditory signal-to-noise ratio. In line with a commonly-observed multisensory enhancement on speech recognition, audiovisual words were more easily recognized than auditory-only words (recognition thresholds of -15 dB and -12 dB, respectively). We here show that the difficulty of recognizing a particular word, either acoustically or visually, determines the occurrence of inverse effectiveness in audiovisual word integration. Thus, words that are better heard or recognized through lipreading, benefit less from bimodal presentation. Audiovisual performance at the lowest acoustic signal-to-noise ratios (45%) fell below the visual recognition rates (60%), reflecting an actual deterioration of lipreading in the presence of excessive acoustic noise. This suggests that the brain may adopt a strategy in which attention has to be divided between listening and lipreading. (shrink)

The study of human-human communication and the development of computational models for human-agent communication have diverged significantly throughout the last decade. Yet, despite frequently made claims of “super-human performance” in, e.g., speech recognition or image processing, so far, no system is able to lead a half-decent coherent conversation with a human. In this paper, we argue that we must start to re-consider the hallmarks of cooperative communication and the core capabilities that we have (...) developed for it, and which conversational agents need to be equipped with: incremental joint co-construction and mentalizing. We base our argument on a vast body of work on human-human communication and its psychological processes that we reason to be relevant and necessary to take into account when modeling human-agent communication. We contrast those with current conceptualizations of human-agent interaction and formulate suggestions for the development of future systems. (shrink)

In this paper, we study the performance of baseline hidden Markov model (HMM) for segmentation of speech signals. It is applied on single-speaker segmentation task, using Hindi speech database. The automatic phoneme segmentation framework evolved imitates the human phoneme segmentation process. A set of 44 Hindi phonemes were chosen for the segmentation experiment, wherein we used continuous density hidden Markov model (CDHMM) with a mixture of Gaussian distribution. The left-to-right topology with no skip states has been selected (...) as it is effective in speech recognition due to its consistency with the natural way of articulating the spoken words. This system accepts speech utterances along with their orthographic “transcriptions” and generates segmentation information of the speech. This corpus was used to develop context-independent hidden Markov models (HMMs) for each of the Hindi phonemes. The system was trained using numerous sentences that are relevant to provide information to the passengers of the Metro Rail. The system was validated against a few manually segmented speech utterances. The evaluation of the experiments shows that the best performance is obtained by using a combination of two Gaussians mixtures and five HMM states. A category-wise phoneme error analysis has been performed, and the performance of the phonetic segmentation has been reported. The modeling of HMMs has been implemented using Microsoft Visual Studio 2005 (C++), and the system is designed to work on Windows operating system. The goal of this study is automatic segmentation of speech at phonetic level. (shrink)

There is growing recognition in Phaedo scholarship of a parallel between the deuteros plous passage and the introduction to Simmias' speech: both speak of attempting to discover or to learn the truth about things, and then, if that proves impossible, to resort to divine or human logoi, the former being the "safer" of the two. It is contended that that model governs Socrates reply to Cebes: he first tried to discover the truth about causes by himself; then (...) he tried to learn it from Anaxagoras; and then, having failed in both endeavors, he resorted to logoi, both "divine" in the hypothesis of the forms and "human" in the hypothesis of intermediates, the former being the "safest" recourse. Finally, a defense is provided for the thesis that in calling the deuteros plous "deuteros," Socrates is being ironical. (shrink)

We used a new method called “Ghost-in-the-Machine” (GiM) to investigate social interactions with a robotic bartender taking orders for drinks and serving them. Using the GiM paradigm allowed us to identify how human participants recognize the intentions of customers on the basis of the output of the robotic recognizers. Specifically, we measured which recognizer modalities (e.g., speech, the distance to the bar) were relevant at different stages of the interaction. This provided insights into human social behavior necessary (...) for the development of socially competent robots. When initiating the drink-order interaction, the most important recognizers were those based on computer vision. When drink orders were being placed, however, the most important information source was the speech recognition. Interestingly, the participants used only a subset of the available information, focussing only on a few relevant recognizers while ignoring others. This reduced the risk of acting on erroneous sensor data and enabled them to complete service interactions more swiftly than a robot using all available sensor data. We also investigated socially appropriate response strategies. In their responses,the participants preferred to use the same modality as the customer’s requests, e.g., they tended to respond verbally to verbal requests. Also, they added redundancy to their responses, for instance by using echo questions. We argue that incorporating the social strategies discovered with the GiM paradigm in multimodal grammars of human–robot interactions improves the robustness and the ease-of-use of these interactions, and therefore provides a smoother user experience. (shrink)

The effects of lexical context on phonological processing are pervasive and there have been indications that such effects may be modulated by attention. However, attentional modulation in speech processing is neither well documented nor well understood. Experiment 1 demonstrated attentional modulation of lexical facilitation of speech sound recognition when task and critical stimuli were identical across attention conditions. We propose modulation of lexical activation as a neurophysiologically plausible computational mechanism that can account for this type of modulation. (...) Contrary to the claims of critics, this mechanism can account for attentional modulation without violating the principle of interactive processing. Simulations of the interactive TRACE model extended to include two different ways of modulating lexical activation showed that each can account for attentional modulation of lexical feedback effects. Experiment 2 tested conflicting predictions from the two implementations and provided evidence that is consistent with bias input as the mechanism of attentional control of lexical activation. (shrink)

Proposing his conception of narrative identity in Oneself as Another, Paul Ricoeur holds that human life is comprehensible, once the story of a man’s life has actually been told, and it is the narrative of one’s life which constructs one’s identity. Developing his theory of heteroglossia and the polyphony of human speech, explicated chiefly in Speech Genres and The Dialogic Imagination, Mikhail Bakhtin recognizes the intrinsically intertwining character of utterance and response. According to him, utterance is (...) always addressed to someone and antedates an answer. Bakhtin’s “addressivity,” as well as his view of discourse as fundamentally dialogic, are convergent with Ricoeur’s elucidation both of man’s answerability to the Other and of narrative identity. The dynamic character of narrative identity, as construed by Ricoeur, converges with the dynamic nature of language as viewed by Bakhtin. The aim of this article is to study the intersections of Ricoeur’s narrative theory and Bakhtin’s recognition of the polyphonic nature of speech. I view these as inherently interrelated, and as testifying, respectively, to the philosophical and linguistic aspects of one and the same phenomenological vision. That vision accounts for selfhood, understood as vulnerable and contextualized, while also recognizing that it is conveyed by means of language with its essentially dialogic openness. (shrink)

Proposing his conception of narrative identity in Oneself as Another, Paul Ricœur holds that human life is comprehensible, once the story of a man’s life has actually been told, and it is the narrative of one’s life which constructs one’s identity. Developing his theory of heteroglossia and the polyphony of human speech, explicated chiefly in Speech Genres and The Dialogic Imagination, Mikhail Bakhtin recognizes the intrinsically intertwining character of utterance and response. According to him, utterance is (...) always addressed to someone and antedates an answer. Bakhtin’s “addressivity,” as well as his view of discourse as fundamentally dialogic, are convergent with Ricœur’s elucidation both of man’s answerability to the Other and of narrative identity. The dynamic character of narrative identity, as construed by Ricœur, converges with the dynamic nature of language as viewed by Bakhtin. The aim of this article is to study the intersections of Ricœur’s narrative theory and Bakhtin’s recognition of the polyphonic nature of speech. I view these as inherently interrelated, and as testifying, respectively, to the philosophical and linguistic aspects of one and the same phenomenological vision. That vision accounts for selfhood, understood as vulnerable and contextualized, while also recognizing that it is conveyed by means of language with its essentially dialogic openness. (shrink)

Proposing his conception of narrative identity in Oneself as Another, Paul Ricœur holds that human life is comprehensible, once the story of a man’s life has actually been told, and it is the narrative of one’s life which constructs one’s identity. Developing his theory of heteroglossia and the polyphony of human speech, explicated chiefly in Speech Genres and The Dialogic Imagination, Mikhail Bakhtin recognizes the intrinsically intertwining character of utterance and response. According to him, utterance is (...) always addressed to someone and antedates an answer. Bakhtin’s “addressivity,” as well as his view of discourse as fundamentally dialogic, are convergent with Ricœur’s elucidation both of man’s answerability to the Other and of narrative identity. The dynamic character of narrative identity, as construed by Ricœur, converges with the dynamic nature of language as viewed by Bakhtin. The aim of this article is to study the intersections of Ricœur’s narrative theory and Bakhtin’s recognition of the polyphonic nature of speech. I view these as inherently interrelated, and as testifying, respectively, to the philosophical and linguistic aspects of one and the same phenomenological vision. That vision accounts for selfhood, understood as vulnerable and contextualized, while also recognizing that it is conveyed by means of language with its essentially dialogic openness. (shrink)

Saffran, Newport, and Aslin (1996a) found that human infants are sensitive to statistical regularities corresponding to lexical units when hearing an artificial spoken language. Two sorts of segmentation strategies have been proposed to account for this early word‐segmentation ability: bracketing strategies, in which infants are assumed to insert boundaries into continuous speech, and clustering strategies, in which infants are assumed to group certain speech sequences together into units (Swingley, 2005). In the present study, we test the predictions (...) of two computational models instantiating each of these strategies i.e., Serial Recurrent Networks: Elman, 1990; and Parser: Perruchet & Vinter, 1998 in an experiment where we compare the lexical and sublexical recognition performance of adults after hearing 2 or 10 min of an artificial spoken language. The results are consistent with Parser’s predictions and the clustering approach, showing that performance on words is better than performance on part‐words only after 10 min. This result suggests that word segmentation abilities are not merely due to stronger associations between sublexical units but to the emergence of stronger lexical representations during the development of speech perception processes. (shrink)

There has been a debate, popularized by Ifenyi Menkiti and Kwame Gyekye, regarding philosophical understandings of the human person in Africa. The debate revolves around the saying "So and so is not a person." Gyekye convincingly argues that the saying is a manner of speech, intended to be a moral evaluation of a person's actions. Menkiti, however, goes further and suggests that many of the African conceptions of a person are based on a dynamic understanding of the self. (...) Similar findings were made of Maasai concepts of personhood in the study done by Patrick Dikirr. The "potential self" is not given but is always being constructed by one's deeds and one's relations. While it is certainly a romanticization to imagine Africans as harmoniously communal in contrast to the "isolated individuals" who supposedly inhabit the West, nevertheless, for a Maasai one gains self-identity and status to a large extent through one's relations to other persons and to animals. However, the paper illustrates the many ways in which Maasais still emphasize individual achievement and thought, debunking notions of African "group-think" and conformity. The paper then draws on Axel Honneth's insights into "recognition" to note the extent to which Maasai recognition of each other prevents the community's capitulation to the value system of other ethnic groups in Kenya or Western influence. (shrink)

Currently, the most used method to measure brain activity under a non-invasive procedure is the electroencephalogram (EEG). This is because of its high temporal resolution, ease of use, and safety. These signals can be used under a Brain Computer Interface (BCI) framework, which can be implemented to provide a new communication channel to people that are unable to speak due to motor disabilities or other neurological diseases. Nevertheless, EEG-based BCI systems have presented challenges to be implemented in real life situations (...) for imagined speech recognition due to the difficulty to interpret EEG signals because of their low signal-to-noise ratio (SNR). As consequence, in order to help the researcher make a wise decision when approaching this problem, we offer a review article that sums the main findings of the most relevant studies on this subject since 2009. This review focuses mainly on the pre-processing, feature extraction, and classification techniques used by several authors, as well as the target vocabulary. Furthermore, we propose ideas that may be useful for future work in order to achieve a practical application of EEG-based BCI systems toward imagined speech decoding. (shrink)

In the following study the author goes back to the beginnings of the Women's Rights movements in order to pose the question on gender equality by approaching it through the prism of language as a powerful tool in human rights battles. This permits her to show the deep interrelation between women's struggle for recognition and some particular women rights, like the "feminization" of professional titles and the implementation of a gender sensitive language. Hence she argues the thesis that (...) even in the most advanced European democracies, where freedom of speech, education, and scientific research seem actually to be legally guaranteed as universal rights, there is still a deep conflict in regard to the use of language and also that we have throughout history backlashes, reproductions of past stereotypes, and a loss of women's rights which were previously acquired. (shrink)

This article aims to present the problem of the impact of artificial intelligence on respect for human dignity in the sphere of care for people who, for various reasons, are described as particularly vulnerable, especially seniors and people with various disabilities. In recent years, various initiatives and works have been undertaken on the European scene to define the directions in which the development and use of artificial intelligence should go. According to the human-centric approach, artificial intelligence should be (...) developed, used and monitored with people in mind, their needs and rights. It is artificial intelligence that should adapt to the rules set by people, not the other way around. The source of all human rights and freedoms is respect for human dignity. This is evidenced by numerous European, international, national regulations and documents. Respecting this value is also one of the works on the AI development. One of the areas of our life into which AI enters more and more boldly and which in the near future, due to demographic reasons and aging population, may be almost dominated by modern technologies, is the medical and care system. The usefulness of industrial co-robots made us think about the possibility of using similar devices in the non-industrial sphere, including in the sphere of care, nursing and therapy. Actually, despite the impressive development of technology and AI, intelligent robots/devices used for care, nursing or therapy (so-called care/nursing or therapy robots) perform rather simple activities. They provide information, navigate, bring something and give it (e.g. medicines, food), help to get up. They are an important support for medical staff in this respect. Performing tasks that are more complex in terms of technology and movement, such as feeding, washing, intimate hygiene, dressing or undressing, is still beyond the reach of the currently used robots. The question we must ask ourselves is whether, in the future, care robots will be able to perform such tasks, and should they do so? Will this lead to the replacement of human medical personnel in the performance of the above-mentioned activities with such robots. Would anyone want to be "looked after" by a soulless machine? Should potential wards/patients, in other words all of us in fact, have the right to object to being taken into the care of an intelligent robot? There is no doubt that intelligent robots have great potential, especially when it comes to ensuring or maintaining the independence or mobility of people with various health limitations. However, we must not forget about the risks and dangers they may generate. Even a simple robot generates serious legal and ethical problems. Who should be liable for damage caused in connection with improper care performed with the participation of a robot? Robots based on AI systems are able to collect a significant amount of sensitive data, for example through the face and speech recognition function. There is considerable scope for abuse in this area, not only in terms of personal data protection but also in terms of informational self-determination, privacy and dignity. Bearing in mind the above risks, the creation of a new right to object to being looked after by a robot should be considered. (shrink)

Top-down feedback does not benefit speech recognition; on the contrary, it can hinder it. No experimental data imply that feedback loops are required for speech recognition. Feedback is accordingly unnecessary and spoken word recognition is modular. To defend this thesis, we analyse lexical involvement in phonemic decision making. TRACE (McClelland & Elman 1986), a model with feedback from the lexicon to prelexical processes, is unable to account for all the available data on phonemic decision making. (...) The modular Race model (Cutler & Norris 1979) is likewise challenged by some recent results, however. We therefore present a new modular model of phonemic decision making, the Merge model. In Merge, information flows from prelexical processes to the lexicon without feedback. Because phonemic decisions are based on the merging of prelexical and lexical information, Merge correctly predicts lexical involvement in phonemic decisions in both words and nonwords. Computer simulations show how Merge is able to account for the data through a process of competition between lexical hypotheses. We discuss the issue of feedback in other areas of language processing and conclude that modular models are particularly well suited to the problems and constraints of speech recognition. Key Words: computational modeling; feedback; lexical processing; modularity; phonemic decisions; reading; speech recognition; word recognition. (shrink)

Cochlear implants (CI) support the development of oral language in hearing-impaired children. However, even with CI, speech recognition in noise (SRiN) is limited. This raised the question, whether these restrictions are related to the quality of life (QoL) of children and adolescents with CI and how SRiN and QoL are related to each other. As a result of a systematic literature research only three studies were found, indicating positive moderating effects between SRiN and QoL of young CI users. (...) Thirty studies addressed the quality of life of children and adolescents with CI. Following the criteria of the World Health Organization (WHO) for pediatric HRQoL (1994) only a minority used validated child centered and age appropriate QoL instruments. Moreover, despite the consensus that usually children and adolescents are the most prominent informants of their own QoL (parent- reports complement the information of the children) only a minority of investigators used self-reports. Restricted SRiN may be a burden for the QoL of children and adolescents with CI. Up to now the CI community does not seem to be aware of a possible impairment of QoL in young CI users. Further studies addressing this topic are urgently needed, which is also relevant for parents, clinicians, therapists, teachers, and politicians. Additionally investigators should use valid pediatric QoL instruments. They should be aware, that most of the young CI users are able to inform about their quality of life also themselves (additionally to parents). (shrink)

Speech recognition is an interdisciplinary subfield of natural language processing (NLP) that facilitates the recognition and translation of spoken language into text by machine. Speech recognition plays an important role in digital transformation. It is widely used in different areas such as education, industry, and healthcare and has recently been used in many Internet of Things and Machine Learning applications. The process of speech recognition is one of the most difficult processes in computer (...) science. Despite numerous searches in this domain, an optimal method for speech recognition has not yet been found. This is due to the fact that there are many attributes that characterize natural languages and every language has its particular highlights. The aim of this research is to provide a comprehensive understanding of the various techniques within the domain of Speech Recognition through a systematic literature review of the existing work. We will introduce the most significant and relevant techniques that may provide some directions in the future research. (shrink)

To solve a series of pension problems caused by aging, based on the emotional recognition of the Internet of Things, the control method and system research of smart homes are proposed. This article makes a detailed analysis and research on the necessity, feasibility, and how to realize speech emotion recognition technology in smart families, introduces the definition and classification of emotion, and puts forward five main emotions to be recognized in speech emotion recognition based on (...) smart family environment. Then, based on this, it analyses the acquisition methods of emotional speech data. On this premise, this article discusses and analyses the related problems of voice data acquisition in smart homes, such as the voice characteristics and acquisition methods, puts forward three rules for voice text design, and determines the relatively suitable hybrid recording acquisition method applied in a smart home environment. At the same time, the design and establishment process of intelligent family emotional speech database is described in detail. The related problems of feature extraction in speech emotion recognition are studied. Starting from the definition of feature extraction, this article expounds on the necessity of feature extraction in the process of recognition and analyses the characteristics of the speech signals. For the specific environment of the smart family, the speech signal required to be processed needs to be close to the auditory characteristics of the human ears, and the speech signal contains enough information. Finally, the Mel frequency cepstrum coefficient is selected as the feature parameter applied in this article, and the extraction process of MFCC is introduced in detail. (shrink)

This volume reflects on questions of human rights in the context of globalization. The essays responding to this subject are rich and varied: they focus on legal acceptance as well as consequences of human rights with regard to social rights and the necessary protection of the environment connected or close to those rights. Another approach to the subject featured in the volume is the legal recognition and the consideration of human rights as moral rights. With concepts (...) on universality, a new cosmopolitism, cultural pluralism and perspectives on religious variants and technological advances the debate on human rights is concluded. Index / Indice I. The Foundation of Rights / El fundamento de los derechos: J. Rubio Carracedo: El legalismo occidental: un problema para la universalizacion de los derechos humanos J. A. Seoane: El caracter juridico de las leyes injustas M. Borowski: Classifying and qualifying properties of fundamental rights J. von Platz: Faith in reason: questioning practical reason as fact, norm, end, and function of human rights R. Cruft: Policy implications of the philosophical foundations of human rights Maria Gabriela Scataglini: Derechos humanos: otra mirada sobre la cuestion de su universalidad II. A New Cosmopolitism / Un nuevo cosmopolitismo: V. de P. Barreto: Derecho cosmopolita y derechos humanos Luis Fernando Barzotto: Reconocimiento y fraternidad A. R. Bernstein: Justifying universal human rights via Rawlsian public reason H. Khatchadourian: Democracy and the globalization of human rights Milagros Otero Parga: Derechos humanos y globalizacion Halim Bepari: Human rights and the United Nations Miracy B. S. Gustin: Rescate de los derechos humanos en situaciones adversas de paises perifericos III. Multiculturism and Pluralism of Values /Multiculturalidad y pluralismo de valores: M. Herrero / M. Saralegui: Dogmatismo politico y pluralismo religioso Berry Tholen: Migration, justice, and Europe's identity. The relevance of special ties for EU's migration policy C. M. Alvarez Chicano: Implicaciones de la inconmensurabilidad de los valores en la aplicacion de principios de orden etico y juridico L. Perez Conejo: Axiologia juridica y pluralismo de valores: algunas consideraciones sobre la nocion de 'inconmensurabilidad' S-I. Liu: Fundamental legal values and globalization with regard to historical experience in freedom of speech L. C. Amezua: Libertad natural y esclavitud voluntaria: reflexiones de F. Suarez sobre la esclavitud IV. Rights and Gender / Derechos y genero: J.M. Gil Ruiz: La violencia de genero en el contexto familiar y la agresion de la ciencia juridica A. Goiriena Lekue: El interes superior del/la nino/a, coparentalidad y neutralidad de genero en la atribucion de la guarda y custodia de los hijos e hijas P. Garcia Guevara: Las politicas de genero en los paises globalizados del 'Tercer Mundo' V. Technological Impact and New Generation Rights / Impacto tecnologico y nuevas generaciones de derechos: M. Ahteensuu: Understanding the precautionary principle in the light of the traditional weak-strong distinction: a critical comment Cristina Monero Atienza: Bases para una teoria de los derechos en el siglo XXI M. Kitahara: Personal data processing and business ethics: personal data processing and personal data protection in business A. D. Oliver Lalana: El lado empirico de la legitimidad comunicativa: sobre la publicidad organizativa del derecho de proteccion de datos en internet J. Ebbesson / M. Zamboni: Transbordering democracy? The case of environment protection Sagrario Moles Nieto: El impacto medioambiental de los cultivos transgenicos VI. Bioethics and Law / Bioetica y derecho: M. Cruz Diaz de Teran: Principios para una biojuridica global C. Faralli / S. Tugnoli: Bioethics between law and science: the Italian law on medically assisted procreation (Law No. 40, February 19, 2004) N. Zeegers: The working of power in communicative regulation: the case of research with human embryos J. A. Fernandez Suarez: La bioetica en la era de la globalizacion F. J. Blazqez Ruiz: Privacidad y discriminacion genetica: propuestas bioeticas y juridicas ante los desafios de un mundo globalizado. (shrink)

We provide a short review on the recent and near-future developments of computational processing of emotion in the voice, highlighting self-learning of representations moving continuously away from traditional expert-crafted or brute-forced feature representations to end-to-end learning, a movement towards the coupling of analysis and synthesis of emotional voices to foster better mutual understanding, weakly supervised learning at a large scale, transfer learning from related domains such as speech recognition or cross-modal transfer learning, and reinforced learning through interactive applications (...) at a large scale. For each of these trends, we shortly explain their implications and potential use such as for interpretation in psychological studies and usage in digital health and digital psychology applications. We also discuss further potential development. (shrink)

Hidden Markov models (HMMs) have been successful for modelling the dynamics of carefully dictated speech, but their performance degrades severely when used to model conversational speech. Since speech is produced by a system of loosely coupled articulators, stochastic models explicitly representing this parallelism may have advantages for automatic speech recognition (ASR), particularly when trying to model the phonological effects inherent in casual spontaneous speech. This paper presents a preliminary feasibility study of one such model (...) class: loosely coupled HMMs. Exact model estimation and decoding is potentially expensive, so possible approximate algorithms are also discussed. Comparison of one particular loosely coupled model on an isolated word task suggests loosely coupled HMMs merit further investigation. An approximate algorithm giving performance which is almost always statistically indistinguishable from the exact algorithm is also identified, making more extensive research computationally feasible. (shrink)

Emotional recognition has arisen as an essential field of study that can expose a variety of valuable inputs. Emotion can be articulated in several means that can be seen, like speech and facial expressions, written text, and gestures. Emotion recognition in a text document is fundamentally a content-based classification issue, including notions from natural language processing (NLP) and deep learning fields. Hence, in this study, deep learning assisted semantic text analysis (DLSTA) has been proposed for human (...) emotion detection using big data. Emotion detection from textual sources can be done utilizing notions of Natural Language Processing. Word embeddings are extensively utilized for several NLP tasks, like machine translation, sentiment analysis, and question answering. NLP techniques improve the performance of learning-based methods by incorporating the semantic and syntactic features of the text. The numerical outcomes demonstrate that the suggested method achieves an expressively superior quality of human emotion detection rate of 97.22% and the classification accuracy rate of 98.02% with different state-of-the-art methods and can be enhanced by other emotional word embeddings. (shrink)

This paper implements the continuous Hindi Automatic Speech Recognition (ASR) system using the proposed integrated features vector with Recurrent Neural Network (RNN) based Language Modeling (LM). The proposed system also implements the speaker adaptation using Maximum-Likelihood Linear Regression (MLLR) and Constrained Maximum likelihood Linear Regression (C-MLLR). This system is discriminatively trained by Maximum Mutual Information (MMI) and Minimum Phone Error (MPE) techniques with 256 Gaussian mixture per Hidden Markov Model(HMM) state. The training of the baseline system has been (...) done using a phonetically rich Hindi dataset. The results show that discriminative training enhances the baseline system performance by up to 3%. Further improvement of ~7% has been recorded by applying RNN LM. The proposed Hindi ASR system shows significant performance improvement over other current state-of-the-art techniques. (shrink)

An excellent introduction to communication theory, this book is a comprehensive study of its subject; fields such as linguistics, logic, mathematics, and psychology are considered in terms of their relevance for communication theory. No material that appeared in the first edition has been deleted from this second edition, but some comments have been added, some figures updated, and the bibliography extended to include the new publications in the field. Cherry begins with an examination of the concept of "communication"; he also (...) discusses social structures, considering the pattern of communication within a social group. After this introductory chapter, Cherry presents an historical review of the evolution of communication theory; he discusses codes, signs, brains, and machines, mentioning the work of such scientists as Shannon, Zipf, and Wiener. Topics such as phonetics, phonemics, object language and metalanguage, Zipf's Law, and semantics are also considered, as well as the role of the telecommunication engineer, who describes and analyzes the physical signals themselves without reference to their meanings. In the concluding chapter, psychological considerations concerning the reception of information are presented: recognition, for example, of universals, of geometric figures, of speech sounds, of distorted speech, the relevance of past experience, and the intake of information by the senses. Although technical material is presented, the book is very readable; jargon is kept at a minimum. The result is an exceedingly interesting and stimulating book.—E. M. (shrink)

The most frequent criticism of the target article is the lack of clear separability of human speech data relative to neuroethological data. A rationalization for this difference was sought in the tinkered nature of such new adaptations as human speech. Basic theoretical premises were defended, and new data were presented to support a claim that speakers maintain a low-noise relationship between F2 transition onset and offset frequencies for stops in pre-vocalic positions through articulatory choices. It remains (...) a viable and testable hypothesis that the phenomenon described by the locus equation is a functional adaptation of production mechanisms to processing preferences of the auditory system. (shrink)

In traditional 3D reconstruction methods, using a single view to predict the 3D structure of an object is a very difficult task. This research mainly discusses human pose recognition and estimation based on 3D multiview basketball sports dataset. The convolutional neural network framework used in this research is VGG11, and the basketball dataset Image Net is used for pretraining. This research uses some modules of the VGG11 network. For different feature fusion methods, different modules of the VGG11 network (...) are used as the feature extraction network. In order to be efficient in computing and processing, the multilayer perceptron in the network model is implemented by a one-dimensional convolutional network. The input is a randomly sampled point set, and after a layer of perceptron, it outputs a feature set of n × 16. Then, the feature set is sent to two network branches, one is to continue to use the perceptron method to generate the feature set of n × 1024, and the other network is used to extract the local features of points. After the RGB basketball sports picture passes through the semantic segmentation network, a picture containing the target object is obtained, and the picture is input to the constructed feature fusion network model. After feature extraction is performed on the RGB image and the depth image, respectively, the RGB feature, the local feature of the point cloud, and the global feature are spliced and fused to form a feature vector of N × 1152. There are three branches for this vector network, which, respectively, predict the object position, rotation, and confidence. Among them, the feature dimensionality reduction is realized by one-dimensional convolution, and the activation function is the ReLU function. After removing the feature mapping module, the accuracy of VC-CNN_v1 dropped by 0.33% and the accuracy of VC-CNN_v2 dropped by 0.55%. It can be seen from the research results that the addition of the feature mapping module improves the recognition effect of the network to a certain extent. (shrink)

Norris, McQueen & Cutler claim that all known speech recognition data can be accounted for with their autonomous model, “Merge.” But this claim is doubly misleading. (1) Although speech recognition is autonomous in their view, the Merge model is not. (2) The body of data which the Merge model accounts for, is not, in their view, speech recognition data. Footnotes1 Author is also affiliated with the Center for the Study of Language and Information, Stanford (...) University, Stanford, CA 94305, [email protected]. (shrink)