Text-Independent, Open-Set Speaker Recognition
Abstract
Speaker recognition, like other biometric personal identification techniques, depends upon a person's intrinsic characteristics. A realistically viable system must be capable of dealing with the open-set task. This effort attacks the open-set task, identifying the best features to use, and proposes the use of a fuzzy classifier followed by hypothesis testing as a model for text-independent, open-set speaker recognition. Using the TIMIT corpus and Rome Laboratory's GREENFLAG tactical communications corpus, this thesis demonstrates that the proposed system succeeded in open-set speaker recognition. Considering the fact that extremely short utterances were used to train the system (compared to other closed-set speaker identification work), this system attained reasonable open-set classification error rates as low as 23% for TIMIT and 26% for GREENFLAG. Feature analysis identified the liftered linear prediction cepstral coefficients with or without the normalized log energy or pitch appended as a robust feature set (based on the 17 feature sets considered), well suited for clean speech and speech degraded by tactical communications channels.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 01, 1996
- Accession Number
- ADA319275
Entities
People
- Stephen V. Pellissier
Organizations
- Air Force Institute of Technology