Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science, Pilani Campus, Vidya Vihar, Pilani, Rajasthan, 333031, India.
J Acoust Soc Am. 2018 Jan;143(1):440. doi: 10.1121/1.5021330.
The difference in fundamental frequency (F0) between talkers is an important cue for speaker segregation. To understand how this cue varies across sound level, Chintanpalli, Ahlstrom, and Dubno [(2014). J. Assoc. Res. Otolaryngol. 15, 823-837] collected level-dependent changes in concurrent-vowel identification scores for same- and different-F0 conditions in younger adults with normal hearing. Modeling suggested that level-dependent changes in phase locking of auditory-nerve (AN) fibers to formants and F0s may contribute to concurrent-vowel identification scores; however, identification scores were not predicted to test this suggestion directly. The current study predicts these identification scores using the temporal responses of a computational AN model and a modified version of Meddis and Hewitt's [(1992). J. Acoust. Soc. Am. 91, 233-245] F0-based segregation algorithm. The model successfully captured the level-dependent changes in identification scores of both vowels with and without F0 difference, as well as identification scores for one vowel correct. The model's F0-based vowel segregation was controlled using the actual F0-benefit across levels such that the predicted F0-benefit matched qualitatively with the actual F0-benefit as a function of level. The quantitative predictions from this F0-based segregation algorithm demonstrate that temporal responses of AN fibers to vowel formants and F0s can account for variations in identification scores across sound level and F0-difference conditions in a concurrent-vowel task.
说话人之间基频 (F0) 的差异是语音分离的一个重要线索。为了了解该线索如何随声级而变化,Chintanpalli、Ahlstrom 和 Dubno [(2014)。J. Assoc. Res. Otolaryngol. 15, 823-837] 收集了具有正常听力的年轻成年人在同一声级和不同声级条件下识别同、不同 F0 元音的得分随声级变化的情况。建模表明,听觉神经纤维(AN)对共振峰和 F0 的相位锁定随声级的变化可能有助于同、不同 F0 元音的识别得分;然而,识别得分并不能直接预测来检验这一假设。本研究使用计算型 AN 模型和 Meddis 和 Hewitt 的 [(1992)。J. Acoust. Soc. Am. 91, 233-245] 基于 F0 的分离算法的修改版来预测这些识别得分。该模型成功地捕捉到了有和没有 F0 差异的元音以及一个元音正确的识别得分随声级变化的情况。该模型的基于 F0 的元音分离使用实际 F0 增益来控制各个声级,从而使得预测的 F0 增益与作为声级函数的实际 F0 增益在定性上匹配。该基于 F0 的分离算法的定量预测表明,AN 纤维对元音共振峰和 F0 的时间响应可以解释在同、不同 F0 条件下的识别得分随声级的变化。