The length of the test speech greatly influences the performance of GMM-UBM based text-independent speakerrecognition system, for example when the length of valid speech is as short as 1~5 seconds, the performance d...
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The length of the test speech greatly influences the performance of GMM-UBM based text-independent speakerrecognition system, for example when the length of valid speech is as short as 1~5 seconds, the performance decreases significantly because the GMM-UBM based speakerrecognitionmethod is a statistical one, of which sufficient data is the foundation. Considering that the use of text information will be helpful to speakerrecognition, a multi-modelmethod is proposed to improve short-utterancespeakerrecognition (SUSR) in Chinese. We build a few phoneme class models for each speaker to represent different parts of the characteristic space and fuse the scores to fit the test data on the models with the purpose of increasing the matching degree between training models and test utterance. Experimental results showed that the proposed method achieved a relative EER reduction of about 26% compared with the traditional GMM-UBM method.
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