Automatic speaker recognition using low resources: Experiments in feature reduction and learning
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The main objective of this thesis is to explore experiments about reduction of computations involved in the Automatic Speaker Recognition (ASR) task and about generating representations for speakerrelated information from speech data automatically. ASR systems heavily depend on the features used for representation of speech information. Over the years, there has been a continuous effort to generate features that can represent speech as best as possible. This has led to the use of larger feature sets in speech and speaker recognition systems. However, with the increasing size of the feature set, it may not necessarily be true that all features are equally important for speech representation. We investigate the relevance of individual features in one of popular feature sets, MFCCs.
Supervisor: Pradip K Das
COMPUTER SCIENCE AND ENGINEERING