噪声环境下非听杂音增强系统的目标语音评价

Sakura Tsuruta, Kou Tanaka, T. Toda, Graham Neubig, S. Sakti, Satoshi Nakamura
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引用次数: 1

摘要

非听杂音(NAM)是用NAM麦克风通过身体传导录下的一种轻声细语。不结盟运动允许无声语音通信,因为它使说话者能够以听不见的声音传达他们的信息。然而,由于身体传导引起的声学变化,其可理解性和自然性与自然语音相比明显下降。为了解决这个问题,已经提出了从NAM到正常语音(NAM- To - speech)和到低声语音(NAM- To - whisper)的统计语音转换(VC)方法。有报道称,这些NAM增强方法显著提高了NAM的语音质量和可理解性,并且NAM-to- whisper比NAM-to- speech更有效。然而,如果听者在嘈杂的环境中听增强语音,哪种方法更有效,这在无声语言交流中经常发生。在本文中,我们假设一个典型的情况,即在安静的环境中由说话者发出非NAM,并在嘈杂的环境中传达给听者,我们研究了什么样的目标语音对非NAM增强更有效。我们还提出了不发音增强方法,将不发音转换为其他类型的目标语音。实验表明,在嘈杂环境下,将语音转换成浊音比将语音转换成浊音更能有效地生成可理解的语音。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An evaluation of target speech for a nonaudible murmur enhancement system in noisy environments
Nonaudible murmur (NAM) is a soft whispered voice recorded with NAM microphone through body conduction. NAM allows for silent speech communication as it makes it possible for the speaker to convey their message in a nonaudible voice. However, its intelligibility and naturalness are significantly degraded compared to those of natural speech owing to acoustic changes caused by body conduction. To address this issue, statistical voice conversion (VC) methods from NAM to normal speech (NAM-to-Speech) and to a whispered voice (NAM-to-Whisper) have been proposed. It has been reported that these NAM enhancement methods significantly improve speech quality and intelligibility of NAM, and NAM-to-Whisper is more effective than NAM-to-Speech. However, it is still not obvious which method is more effective if a listener listens to the enhanced speech in noisy environments, a situation that often happens in silent speech communication. In this paper, assuming a typical situation in which NAM is uttered by a speaker in a quiet environment and conveyed to a listener in noisy environments, we investigate what kinds of target speech are more effective for NAM enhancement. We also propose NAM enhancement methods for converting NAM to other types of target voiced speech. Experiments show that the conversion process into voiced speech is more effective than that into unvoiced speech for generating more intelligible speech in noisy environments.
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