Robust speaker verification by combining MFCC and entrocy in noisy conditions

Automatic speaker recognition may achieve remarkable performance in matched training and test conditions. Conversely, results drop significantly in incompatible noisy conditions. Furthermore, feature extraction significantly affects performance. Mel-frequency cepstral coefficients MFCCs are most commonly used in this field of study. The literature has reported that the conditions for training and testing are highly correlated. Taken together, these facts support strong recommendations for using MFCC features in similar environmental conditions (train/test) for speaker recognition. However, with noise and reverberation present, MFCC performance is not reliable. To address this, we propose a new feature 'entrocy' for accurate and robust speaker recognition, which we mainly employ to support MFCC coefficients in noisy environments. Entrocy is the fourier transform of the entropy, a measure of the fluctuation of the information in sound segments over time. Entrocy features are combined with MFCCs to generate a composite feature set which is tested using the gaussian mixture model (GMM) speaker recognition method. The proposed method shows improved recognition accuracy over a range of signal-to-noise ratios.

A robust speaker recognition based on Data augmentation

The authors have requested that this preprint be withdrawn due to erroneous posting.

A robust speaker recognition based on Data augmentation

It is known that large-scale training data can get the better effect of recognition. However, it is difficult to collect a lot of labeled training data for speaker recognition. At the same time, the performance of speaker recognition is greatly influenced by environmental noise. In this paper, we use data augmentation by adding noise to get much training data and improve the robustness of speaker recognition. The experimental results demonstrate that data augmentation have the better performance improvement on Chinese-863 database.