2023/08/22 by Nguyen, Thai-Binh, Waibel, Alexander
#Audio and Speech Processing (eess.AS) #Computation and Language (cs.CL) #FOS: Computer and information sciences #FOS: Electrical engineering #Sound (cs.SD) #electronic engineering #information engineering
paper · doi:10.48550/arxiv.2308.11380
This paper presents an end-to-end model designed to improve automatic speech recognition (ASR) for a particular speaker in a crowded, noisy environment. The model utilizes a single-channel speech enhancement module that isolates the speaker's voice from background noise (ConVoiFilter) and an ASR module. The model can decrease ASR's word error rate (WER) from 80% to 26.4% through this approach. Typically, these two components are adjusted independently due to variations in data requirements. However, speech enhancement can create anomalies that decrease ASR efficiency. By implementing a joint fine-tuning strategy, the model can reduce the WER from 26.4% in separate tuning to 14.5% in joint tuning. We openly share our pre-trained model to foster further research hf.co/nguyenvulebinh/voice-filter.