Speech Enhancement Based on Deep AutoEncoder for Remote Arabic Speech Recognition

Remote applications that deal with speech need the speech signal to be compressed. First, speech coding transforms the continuous waveform into a numerical form. Then, the digitized signal is compressed with or without loss of information. This transformation affects the original waveform and degrades performances for further recognition of the speech signal. Meanwhile, the transmission is another source of speech degradation. To restore the original “clean” speech, speech enhancement (SE) is widely used, and deep learning algorithms are state-of-the-art, nowadays. In this paper, the target application is a remote Arabic speech recognition system, and the aim of using SE is to improve the accuracy of the speech recognizer. For that purpose, a Deep Auto Encoder (DAE) is used. The effect of the DAE-based SE is studied through different configurations, and the performances are evaluated through accuracy. The results showed an improvement of about 3.17 between the accuracy prior to the SE and that computed with the enhanced speech.