Sub-word unit talker verification using hidden Markov models

A talker verification system based on characterizing talker utterances as sequences of subword units represented by hidden Markov models (HMMs) was implemented and tested. Two types of subword units were studied: phonelike units (PLUs) and acoustic segment units (ASUs). PLUs are based on phonetic transcriptions of spoken utterances and ASUs are extracted directly from the acoustic signal without use of any linguistic knowledge. The ASU representation has the advantage of not requiring transcriptions of training utterances. Verification performance was evaluated on a 100-talker database of 20000 isolated digit utterances. The experiments show only small differences in performance between PLU- and ASU-based representations. Overall, the verification equal-error rate is approximately 7 to 8% for one-digit test utterances (approximately 0.5 s in duration) and 1% or less for seven-digit test utterances (approximately 3.5 s in duration). In addition, a technique for updating models, using data from current test utterances, was devised and implemented. Using this adaptation technique, the error rate falls to 6% for one-digit utterances and less than 0.5% for seven-digit utterances. The experiment confirms that excellent verification performance can be obtained using HMMs of subword units