A Self-Detection and Self-Repair Methodology for Reliable Speech Recognition Considering AWGN Noises

Abstract

Speech recognition is expected to be widely used in many IoT applications such as smart home and wearable devices. For successful speech recognition, the integrity of voice signals is critical. However, during transmission or storage of voice signals, there are usually noises, which may distort the voice signals and thus invalidate the speech recognition process, especially when the noises are significant. In this paper we address the issue of detecting and repairing noisy audio signals. In particular, self-detection and self-repair approaches are investigated such that on-line detection and repair is possible. In this work the AWGN noise model that is widely used in the communication area is employed to model noises. The speech recognition application is targeted such that as many correct words can be recognized as possible for the repaired audio. To the best of our knowledge, this work is the first one that addresses the audio self-detection and self-repair problem considering AWGN noises together with the speech recognition application. We propose a detection and repair methodology that can accurately detect noisy audio signals, and utilize the noise-free ones to predict the expected values of noisy audio signals for repairing. No additional golden signals are required. Compared with the related method in the literature, the proposed methodology has much higher repair effectiveness. Our experimental results show that 45.93% enhancement on the speech recognition rate can be achieved on average. As a comparison, for the previous method, the recognition rate can be enhanced by only 11.68%.