Concatenated phoneme models for text-variable speaker recognition

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Abstract
Methods that create models to specify both speaker and phonetic information accurately by using only a small amount of training data for each speaker are investigated. For a text-dependent speaker recognition method, in which arbitrary key texts are prompted from the recognizer, speaker-specific phoneme models are necessary to identify the key text and recognize the speaker. Two methods of making speaker-specific phoneme models are discussed: phoneme-adaptation of a phoneme-independent speaker model and speaker-adaptation of universal phoneme models. The authors also investigate supplementing these methods by adding a phoneme-independent speaker model to make up for the lack of speaker information. This combination achieves a rejection rate as high as 98.5% for speech that differs from the key text and a speaker verification rate of 100.0%.

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