A Deep Learning Speech Enhancement Architecture Optimised for Speech Recognition and Hearing Aids

Abstract

With the fast progression of the speech enhancement field after the introduction of deep learning techniques, there is a need to consider the adjustments needed to employ these techniques for real-life applications. In this work, we present an optimised deep learning speech enhancement architecture for automatic speech recognition and hearing aids, two key speech enhancement applications. A speech enhancement architecture with a signal-to-noise ratio switch is presented for automatic speech recognition systems, to avoid denoising artifacts that cause performance degradation in the case of clean or high signal-tonoise speech. Moreover, a smart speech enhancement architecture is presented for hearing aids to retain important emergency noise in the audio signal. The presented work achieved 13.9% reduction in the word error rate of an automatic speech recognition system. Additionally, the smart speech enhancement architecture resulted in 0.18 improvement in HAAQI audio quality metric.