. Speaker recognition has been an active research area for many years. Methods to represent and quantify information embedded in speech signal are termed as features of the signal. The features are obtained, modeled and stored for further reference when the system is to be tested. Decision whether to accept or reject speakers are taken based on parameters of the data modeling techniques. Real world offers various degradations to the signal that hamper the signal quality. The degradations may be due (...) to ambient background noise, reverberation or multispeaker scenario. This paper presents a survey of various feature extraction, data modeling methods, metrics that are used to take the decisions and methods that can be used to preprocess the degraded data that have been used to perform the task of speaker recognition. (shrink)

This article presents an overview of the single-channel dereverberation methods suitable for distant speech recognition application. The dereverberation methods are mainly classified based on the domain of enhancement of speech signal captured by a distant microphone. Many single-channel speech enhancement methods focus on either denoising or dereverberating the distorted speech signal. There are very few methods that consider both noise and reverberation effects. Such methods are discussed under a multistage approach in this article. The article concludes with a hypothesis that (...) the methods that do not require an a priori reverberation impulse response is desirable in varying the environmental conditions for DSR applications such as intelligent home and office environments, humanoid robots, and automobiles rather than the methods that require an a priori reverberation impulse response. (shrink)