Blind noise suppression for Non-Audible Murmur recognition with stereo signal processing

2011 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2011-12-01 DOI:10.1109/ASRU.2011.6163981

Shunta Ishii, T. Toda, H. Saruwatari, S. Sakti, Satoshi Nakamura

引用次数: 6

Abstract

In this paper, we propose a blind noise suppression method for Non-Audible Murmur (NAM) recognition. NAM is a very soft whispered voice detected with NAM microphone, which is one of the body-conductive microphones. Due to its recording mechanism, the detected signal suffers from noise caused by speaker's movements. In the proposed method using a stereo signal detected with two NAM microphones, the noise is estimated with blind source separation, and then, spectral subtraction is performed in each channel to reduce the noise. Moreover, channel selection is performed frame by frame to generate less distorted monaural NAM signal. Experimental results show that 1) word accuracy in large vocabulary continuous NAM recognition is degraded from 69.2% to 53.6% by the noise and 2) it is significantly recovered to 63.3% in a simulated situation and 58.6% in a real situation with the proposed method.

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用立体声信号处理盲噪声抑制非听杂音识别

本文提出了一种用于非可听杂音(NAM)识别的盲噪声抑制方法。NAM是一种非常柔和的低声声音，由NAM麦克风检测，它是身体传导麦克风之一。由于其记录机制，检测到的信号受到说话人运动产生的噪声的影响。该方法采用双麦克风检测的立体声信号，采用盲源分离法对噪声进行估计，然后在每个通道进行频谱减法来降低噪声。此外，逐帧进行信道选择以产生畸变较小的单频NAM信号。实验结果表明:(1)噪声使大词汇量连续非NAM识别的词正确率从69.2%下降到53.6%，(2)模拟情景和真实情景下的词正确率分别恢复到63.3%和58.6%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2011 IEEE Workshop on Automatic Speech Recognition & Understanding

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