Speaker adaptation of tied-mixture-based phoneme models for text-prompted speaker recognition

Speaker adaptation methods for tied-mixture-based phoneme models are investigated for text-prompted speaker recognition. For this type of speaker recognition, speaker-specific phoneme models are essential for verifying both the key text and the speaker. This paper proposes a new method of creating speaker-specific phoneme models. This uses speaker-independent (universal) phoneme models consisting of tied-mixture HMMs and adapts the feature space of the tied-mixtures to that of the speaker through phoneme-dependent/independent iterative training. Therefore, it can adapt models of phonemes that have a small amount of training data to the speaker. The proposed method was tested using 15 speakers´ voices recorded over 10 months and achieved a speaker and text verification rate of 99.4% even when both the voices of different speakers and different texts uttered by the true speaker were to be rejected