Yet another Gaussian mixture model-based feature compensation method for robust noisy-digit recognition

We propose yet another Gaussian mixture model (YGMM) for robust speech recognition in noisy environments. The main difference between the proposed method and previously proposed GMM-based methods is that we estimate the noise features instead of the clean-speech features. In the implemented system, a condition classifier, incidentally based on GMM, is used to decide the noise type and level, and the corresponding GMM is employed to compensate for the noise-corrupted features. The proposed method and the implemented system are evaluated with the well-documented Aurora 2.0 noisy digit corpus. The results are promising. Specifically, it achieves a relative improvement in word error rate of 52.4% over the standard baseline, and 24.9% over a better baseline based on a traditional GMM-based feature compensation method.