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Auditory Brainstem Representation of the Voice Pitch Contours in the Resolved and Unresolved Components of Mandarin Tones

机译:普通话的已解析和未解析成分中语音音高轮廓的听觉脑干表示

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摘要

Accurate perception of voice pitch plays a vital role in speech understanding, especially for tonal languages such as Mandarin. Lexical tones are primarily distinguished by the fundamental frequency (F0) contour of the acoustic waveform. It has been shown that the auditory system could extract the F0 from the resolved and unresolved harmonics, and the tone identification performance of resolved harmonics was better than unresolved harmonics. To evaluate the neural response to the resolved and unresolved components of Mandarin tones in quiet and in speech-shaped noise, we recorded the frequency-following response. In this study, four types of stimuli were used: speech with either only-resolved harmonics or only-unresolved harmonics, both in quiet and in speech-shaped noise. Frequency-following responses (FFRs) were recorded to alternating-polarity stimuli and were added or subtracted to enhance the neural response to the envelope (FFRENV) or fine structure (FFRTFS), respectively. The neural representation of the F0 strength reflected by the FFRENV was evaluated by the peak autocorrelation value in the temporal domain and the peak phase-locking value (PLV) at F0 in the spectral domain. Both evaluation methods showed that the FFRENV F0 strength in quiet was significantly stronger than in noise for speech including unresolved harmonics, but not for speech including resolved harmonics. The neural representation of the temporal fine structure reflected by the FFRTFS was assessed by the PLV at the harmonic near to F1 (4th of F0). The PLV at harmonic near to F1 (4th of F0) of FFRTFS to resolved harmonics was significantly larger than to unresolved harmonics. Spearman's correlation showed that the FFRENV F0 strength to unresolved harmonics was correlated with tone identification performance in noise (0 dB SNR). These results showed that the FFRENV F0 strength to speech sounds with resolved harmonics was not affected by noise. In contrast, the response to speech sounds with unresolved harmonics, which were significantly smaller in noise compared to quiet. Our results suggest that coding resolved harmonics was more important than coding envelope for tone identification performance in noise.
机译:准确感知音高在语音理解中起着至关重要的作用,尤其是对于普通话等音调语言而言。词汇音主要由声波的基频(F0)轮廓来区分。结果表明,听觉系统可以从已解析和未解析谐波中提取出F0,解析谐波的音调识别性能要优于未解析谐波。为了评估在安静和语音形噪声中对普通话音的已分解和未分解成分的神经响应,我们记录了频率跟随响应。在这项研究中,使用了四种类型的刺激:在安静和语音形状的噪声中,具有仅分辨出的谐波或只有未分辨出的谐波的语音。记录频率跟随响应(FFR)对交替极性刺激,并添加或减去以增强对包膜(FFRENV)或精细结构(FFRTFS)的神经响应。 FFRENV反映的F0强度的神经表示通过时域中的峰值自相关值和光谱域中F0处的峰值锁相值(PLV)进行评估。两种评估方法均表明,对于包括未解决的谐波的语音,安静状态下的FFRENV F0强度明显强于噪声,但对于包括已解决的谐波的语音,则没有噪声强度。 FFRTFS反映的时间精细结构的神经表示由PLV在F1(F0的4)附近的谐波处评估。 FFRTFS的F1(F0的4阶)附近的谐波的PLV明显大于未解决的谐波。 Spearman的相关性表明,针对未解决谐波的FFRENV F0强度与噪声(0 dB SNR)中的音调识别性能相关。这些结果表明,FFRENV F0对具有已分解谐波的语音的强度不受噪声影响。相反,对语音的响应具有未解决的谐波,与安静相比,其噪声要小得多。我们的结果表明,对于噪声中的音调识别性能,编码解析的谐波比编码包络更为重要。

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