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A Study on Alternative Speech Sensor

机译:替代语音传感器的研究

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摘要

This paper presents a study on alternative speech sensor for speech processing applications. Noise robustness is one of the major considerations in speech processing systems. In presence of noise, speech signal renders unintelligible naturally and thus degrades the performance of automatic speech recognition systems. Close-talk microphone perfums well for clean speech signals. The close-talk microphone based recognition performance fails under real non-stationary conditions and also degraded strongly by the background noise. One way of improving such a system performance is by the use of alternative sensors, which are attached to the speaker's skin and receive the uttered speech through throat or bones. There are two types of sensors namely alternative acoustic and non-acoustic sensors. First, alternative acoustic sensors are more isolated from environmental noise and pick up the speech signal in a robust manner. Second is to develop noisy robust speech recognition system using Multi-sensor approach. This approach combines the information from different sources of acoustic speech sensors. The thirdinvolves non-acoustic speech sensors, which are primarily used for speaker identification task and some speech recognition applications. Fourth, discusses the speech enhancement methods for the noisy speech signal. These approaches help to improve the speech recognition system in noisy conditions and lead to building a robust ASR system.
机译:本文提出了一种用于语音处理应用的替代语音传感器的研究。噪声鲁棒性是语音处理系统中的主要考虑因素之一。在存在噪声的情况下,语音信号自然会变得难以理解,从而降低了自动语音识别系统的性能。近距离麦克风可以很好地表现出清晰的语音信号。基于近距离麦克风的识别性能在实际的非平稳条件下会失败,并且还会由于背景噪声而大大降低。改善这种系统性能的一种方法是使用替代传感器,这些传感器附着在扬声器的皮肤上,并通过喉咙或骨头接收发出的语音。有两种类型的传感器,即声音传感器和非声音传感器。首先,替代性声学传感器与环境噪声之间的距离更小,并且能够以可靠的方式拾取语音信号。其次是使用多传感器方法开发具有噪声的鲁棒语音识别系统。这种方法结合了来自不同声音语音传感器的信息。第三涉及非声学语音传感器,其主要用于说话人识别任务和某些语音识别应用。第四,讨论了嘈杂语音信号的语音增强方法。这些方法有助于改善嘈杂条件下的语音识别系统,并导致构建强大的ASR系统。

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