A PROGRESSIVE LEARNING APPROACH TO ADAPTIVE NOISE AND SPEECH ESTIMATION FOR SPEECH ENHANCEMENT AND NOISY SPEECH RECOGNITION

In this paper, we propose a progressive learning-based adaptive noise and speech estimation (PL-ANSE) method for speech preprocessing in noisy speech recognition, leveraging upon a frame-level noise tracking capability of improved minima controlled recursive averaging (IMCRA) and an utterance-level deep progressive learning of nonlinear interactions between speech and noise. First, a bi-directional long short-term memory model is adopted at each network layer to learn progressive ratio masks (PRMs) as targets with progressively increasing signal-to-noise ratios. Then, the estimated PRMs at the utterance level are combined within a conventional speech enhancement algorithm at the frame level for speech enhancement. Finally, the enhanced speech based on multi-level information fusion is directly fed into a speech recognition system to improve the recognition performance. Experiments show that our proposed approach can achieve a relative word error rate (WER) reduction of 22.1% when compared to results attained with unprocessed noisy speech (from 23.84% to 18.57%) on the CHiME-4 single-channel real test data.