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Segment specific enhancement of speech characteristics for improving speech intelligibility under adverse listening conditions

机译:细分特定的语音特性,以改善不利听音条件下的语音清晰度

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Degradation of speech intelligibility in the presence of noise is a serious problem in speech communication devices. We propose an offline speech enhancement technique based on speech recognition and segment-specific modification of speech characteristics. Hidden Markov model based speech recognition is used for locating fricative segments in vowel-consonant-vowel utterances. Speech characteristic modification involved increasing the intensity of the fricative segment with respect to the nearby vowel segment by 12 dB. Intelligibility of modified speech was evaluated by conducting listening tests using 6 normal hearing subjects with speech-spectrum shaped noise as masker. The results of listening tests indicated statistically significant improvements in fricative recognition scores by 4%, 5%, 6%, and 14%, at SNRs of 12, 6, 0, and -6 dB, respectively.
机译:在噪声存在下语音清晰度的下降是语音通信设备中的严重问题。我们提出了一种基于语音识别和语音特征的段特定修改的离线语音增强技术。基于隐马尔可夫模型的语音识别用于定位元音-辅音-元音发声中的摩擦片段。语音特征修改涉及将相对于附近元音片段的摩擦片段的强度增加12 dB。通过使用6名正常听觉受试者(语音频谱形状的噪声作为掩蔽者)进行听力测试,评估了修饰语音的可理解性。听力测试的结果表明,在SNR为12、6、0和-6 dB时,摩擦识别分数在统计学上有显着提高,分别提高了4%,5%,6%和14%。

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