Date of Award
Master of Science in Electrical Engineering
Department of Electrical and Computer Engineering
Dennis Ruck, PhD
Martin DeSimio, PhD
Timothy Anderson, PhD
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 filtered 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.
DTIC Accession Number
Pellissier, Stephen V., "Text-Independent, Open-Set Speaker Recognition" (1996). Theses and Dissertations. 6208.