基於多視角注意力機制語音增強模型於強健性自動語音辨識

Recently, many studies have found that phase information is crucial in Speech Enhancement (SE), and time-domain single-channel speech enhancement techniques have been proved effective on noise suppression and robust Automatic Speech Recognition (ASR). Inspired by this, this research investigates two recently proposed SE methods that consider phase information in time domain and frequency domain of speech signals, respectively. Going one step further, we propose a novel multi-view attention-based speech enhancement model, which can harness the synergistic power of the aforementioned time-domain and frequency-domain SE methods and can be applied equally well to robust ASR. To evaluate the effectiveness of our proposed method, we use various noise datasets to create some synthetic test data and conduct extensive experiments on the Aishell-1 Mandarin speech corpus. The evaluation results show that our proposed method is superior to some current state-of-the-art time-domain and frequency-domain SE methods. Specifically, compared with the time-domain method, our method achieves 3.4%, 2.5% and 1.6% in relative character error rate (CER) reduction at three signal-to-noise ratios (SNRs), -5 dB, 5 dB and 15 dB, respectively, for the test set of pre-known noise scenarios, while the corresponding CER reductions for the test set of unknown noise scenarios are 3.8%, 4.8% and 2.2%, respectively.