This letter studies feature selection in speaker recognition from an information-theoretic view. We closely tie the performance, in terms of the expected classification error probability, to the mutual information between speaker identity and features. Information theory can then help us to make qualitative statements about feature selection and performance. We study various common features used for speaker recognition, such as mel-warped cepstrum coefficients and various parameterizations of linear prediction coefficients. The theory and experiments give valuable insights in feature selection and performance of speaker-recognition applications.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Applied Mathematics
- Electrical and Electronic Engineering