Speaker adaptation based on spectral normalization and dynamic HMM parameter adaptation

Speaker adaptation has received a considerable amount of attention in recent years. Most of the previous work focused on techniques which require a certain amount of speech to be collected from the target speaker. This paper presents two speaker adaptation methods, including a feature normalization and a HMM parameter adaptation, developed to improve a speaker-independent HMM-based speech recognition system. The proposed adaptation algorithms are text-independent and do not require target speech collection. By applying the feature normalization, the target speech is normalized to reduce the acoustic inter-speaker and environmental variability. By applying the HMM parameter adaptation, the recognition system parameters are dynamically modified to model the target speech. We carried out recognition experiments to assess the performance, using two different speaker-independent recognizers as the baseline systems: a continuous digit recognizer and a keyword recognition system. The results show that when both adaptation techniques are combined, the word error of the digit recognizer using the TI Connected Digit corpus is reduced by about 30% and the detection error of a keyword recognition system using the Road Rally corpora is reduced by about 40%