Speaker identification with user-selected password phrases.

An open-set speaker identification system is described in which general-text, sentence-long phrases are used as passwords. Customers are allowed to select their own password phrases and the system has no knowledge of the text. Passwords are represented by phone transcriptions and whole-phrase Hidden Markov Models (HMM’s). Phrase identification, carried out using both speaker dependent and speaker independent models, constitutes an identity claim. Verification of the claim uses likelihood ratio scoring with speaker independent phone HMM’s providing the background model score. An evaluation has been carried out over a database of password phrases spoken by 250 speakers. 100 of the speakers are test speakers. In an experimental trial, each test speaker is designated as a customer or an imposter and speaks the phrase associated with the customer. The imposter set for each customer consists of same-gender test speakers excluding the customer. At a 5% reject level, the rate of imposter identification is approximately 4%. The misidentification rate for both customers and imposters is less than 0.1%. The closed-set identification error rate is less than 1%, while the average verification equal-error rate is approximately 3%.