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首页> 外文会议>Audio Engineering Society 122nd Convention >Noise-Robust Recognition System Making Use of Body-Conducted Speech Microphone
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Noise-Robust Recognition System Making Use of Body-Conducted Speech Microphone

机译：利用人体传导语音麦克风的鲁棒识别系统

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摘要

In recent years, speech recognition systems have been introduced in a wide variety of environments such as vehicle instrumentation. Speech recognition plays an important role in ships’ chief engineer systems. In such a system, speech recognition supports engine room controls, and lower than 0-dB signal-to-noise ratio (SNR) operability is required. In such a low SNR environment, a noise signal can be misjudged as speech, dramatically decreasing the recognition rate. Hence, speech recognition systems operating in low SNR environments have not received much attention. Therefore, this study focuses on a recognition system that uses body-conducted signals. Such signals are seldom a?ected by background noise, and thus a high recognition rate can be expected in low SNR environments such as an engine room. Since noise is not introducedwithinbody-conductedsignalsthatareconductedinsolids, evenwithinsitessuchasenginerooms which are low SNR environments, construction of a system with a high recognition rate can be expected. However, within the construction of such systems, in order to create models specialized for body-conducted speech, learning data consisting of sentences that must be read in numerous times is required. Therefore, in the present study we applied a method in which the specific nature of body-conducted speech is reflected within an existing speech recognition system with only small numbers of vocalizations. Simultaneously, the measure by pretreatment was also worked on.
机译：近年来，语音识别系统已被引入多种环境中，例如车辆仪表。语音识别在船舶总工程师系统中扮演着重要角色。在这样的系统中，语音识别支持机舱控制，并且要求低于0 dB的信噪比（SNR）可操作性。在如此低的SNR环境中，噪声信号可能会被误判为语音，从而大大降低了识别率。因此，在低SNR环境中运行的语音识别系统并未受到太多关注。因此，本研究着重于使用人体传导信号的识别系统。这样的信号很少受到背景噪声的影响，因此可以在诸如机房之类的低SNR环境中获得很高的识别率。由于不会在固体中传导的体内传导信号中引入噪声，因此即使在诸如信噪比较低的机舱之类的场所，也可以期望构建具有高识别率的系统。然而，在这种系统的构造中，为了创建专门用于身体传导语音的模型，需要学习由必须多次读取的句子组成的数据。因此，在本研究中，我们应用了一种方法，其中仅通过少量发声就可以在现有的语音识别系统中反映出身体传导语音的特殊性质。同时，还进行了预处理措施。

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