Noise Tracking Algorithm for Speech Enhancement

In this paper, the improved noise tracking algorithm for speech enhancement is proposed. This method is used to detect the speech presence probability based on chi square distribution. During speech presence period, the time varying smoothing factor is adjusted. In addition, the estimated noise variance is recursively smoothed then averaged for various noises. This proposed method can track the noise signal with different input SNR (0dB and 5dB) levels. The performance of the proposed and the existing methods are evaluated by various noise conditions. From these evaluated results, it is observed that the proposed method reduces the performance measures as 6% - 58% of MSE and 3% - 97% of LogErr as compared to that of the various existing algorithms under various noise conditions with optimal smoothing factors ap = 0.97 and ad = 0.7. When this is integrated into the speech enhancement, it improves the speech signal quality and intelligibility with less speech distortion and residual noise

Proceedings of International Conference On Global Innovations In Computing Technology (ICGICT'14) Comparison Of Cepstral And Mel Frequency Cepstral Coefficients For Various Clean And Noisy Speech Signals

ABSTRACT: Speech is the most natural form of human communication and speech processing has been one of the most exciting areas of the signal processing. The main goal of speech recognition area is to develop techniques and systems for speech input to machine. Speech recognition can be roughly divided into two stages: feature extraction and classification. Although significant advances have been made in speech recognition technology, it is still a difficult problem to design a speech recognition system for speaker-independent, continuous speech. One of the fundamental questions is whether all of the information necessary to distinguish words is preserved during the feature extraction stage. If vital information is lost during this stage, the performance of the following classification stage is inherently crippled and can never measure up to human capability. Thus, this work finds out an improved feature extraction algorithm based on Mel frequency cepstral coefficient analysis. The results show the comparative analysis of various noise signals and their performance measure using SNR and peak power signal