Waveform-Domain Speech Enhancement Using Spectrogram Encoding for Robust Speech Recognition
Hao Shi, Masato Mimura, Tatsuya Kawahara
Abstract
While waveform-domain speech enhancement (SE) has been extensively investigated in recent years and achieves state-of-the-art performance in many datasets, spectrogram-based SE tends to show robust and stable enhancement behavior. In this paper, we propose a waveform-spectrogram hybrid method (WaveSpecEnc) to improve the robustness of waveform-domain SE. WaveSpecEnc refines the corresponding temporal feature map by spectrogram encoding in each encoder layer. Incorporating spectral information provides robust human hearing experience performance. However, it has a minor automatic speech recognition (ASR) improvement. Thus, we improve it for robust ASR by further utilizing spectrogram encoding information (WaveSpecEnc+) to both the SE front-end and ASR back-end. Experimental results using the CHiME-4 dataset show that ASR performance in real evaluation sets is consistently improved with the proposed method, which outperformed others, including DEMUCS and Conv-Tasnet. Refining in the shallow encoder layers is very effective, and the effect is confirmed even with a strong ASR baseline using WavLM.