Robust Speaker Identification Algorithm Based on Adaptive Parallel Model Combination

The performance of speaker recognition systems degrade rapidly in real applications due to environmental noise．This paper proposes a robust speaker recognition method based on Gaussian Mixture Model-Universal Background Model(GMM-UBM) and adaptive parallel model combination(APMC).APMC feature compensation algorithm,which is robust to noise, can effectively reduce the mismatch between training environment and testing environment so as to improve the recognition accuracy and anti-noise performance.Firstly, automatically estimating noise feature from test speech.Secondly, using a single Gaussian model to fit the feature，then getting the mean and covariance of noise feature.Finally,according to the mean and covariance of noise from the second step,the mean vectors and covariance matrices of the training GMM are transformed to the testing condition by this method as far as possible.The experimental results indicate that the proposed method can reconstruct the clean speech GMM parameters more accurately.Also,this method can significantly improve the speaker identification accuracy,especially in low SNR．