Exploration of Feature Reduction of MFCC Spectral Features in Speaker Recognition

摘要

Most recognition 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 is not necessary that all features are equally important for speech representation. This paper investigates the relevance of individual features in one of popular feature sets, MFCCs. The objective of the study is to identify features which are more important from speech information representation perspective. Experiments were conducted for the task of speaker recognition. Results indicate that it is possible to reduce the feature set size by more than 60 % without significant losses in accuracy.