Neural-network-based HMM adaptation for noisy speech recognition

Abstract

This paper proposes a new method, using neural networks, of adapting phone HMMs to noisy speech. The neural networks are designed to map clean speech HMMs to noise-adapted HMMs, using noise HMMs and signal-to-noise ratios (SNRs) as inputs. The neural network is trained by minimizing the mean square error between the output HMMs and the target noise-adapted HMMs. In an evaluation, the proposed method was used to recognize noisy broadcast-news speech in speaker-dependent and speaker-independent modes. The trained networks were found to be effective in recognizing new speakers under new noise and various SNR conditions.