This paper describes recent improvements to an algorithm for identifying an unknown voice from a set of known voices using unconstrained speech material. These algorithms compare the underlying probability distributions of speech utterances using a method that is free of assumptions regarding the form of the distributions (e.g., Gaussian, etc.). In comparing two utterances, the algorithms accumulate minimum inter-frame distances between frames of the utterances. In recognition tests on the Switchboard database, using a closed population of speakers, we show that the new algorithm performs substantially better than the baseline algorithm. The modifications are segment-based scoring, limiting likelihood ratio estimates for robustness and estimating biases associated with reference files. >
['Lawrence G. Bahler', 'Jack E. Porter', 'Alan L. Higgins']
Improved voice identification using a nearest-neighbor distance measure