Research on Noise Processing and Speech Frame Classification Model of Noisy Short Utterance Signal Processing

The noise processing is the key of improving recognition rate for the noisy utterance. While for the short utterance, its corpus is less and small amount of speech data is available for testing and training, so making full use of its limit corpus is the key of improving recognition rate of the short utterance. For the noisy short utterance, the noise processing and making full use of the limit corpus are vital. We proposed noise separation algorithm based on constrained Non-negative matrix factorization (CNMF) to make the noise processing. As making full use of the limit corpus, we proposed the improved SNR discrimination algorithm (ISNRDA) and the differences detection and discrimination algorithm (DDADA), we use the two classification algorithm to estimate the quality of the speech frame, and classify the speech frame. Besides, we combine the above classification result with the GMM-UBM three-stage classification model proposed in this paper, so that we can make full use of the limit corpus of the noisy short utterance. Experiments show that the above algorithms can improve speaker recognition performance of noisy short utterance.