Evidence map›Paper›PMID 35937895›Full record

ArticleFrontiers in neuroscience2022

Towards Evaluating Pitch-Related Phonation Function in Speech Communication Using High-Density Surface Electromyography.

Mingxing Zhu, Xin Wang, Hanjie Deng, Yuchao He, Haoshi Zhang, Zhenzhen Liu, Shixiong Chen, Mingjiang Wang, Guanglin Li

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Mingxing ZhuSchool of Electronic and Information Engineering, Harbin Institute of Technology, Shenzhen, China.
Xin WangCAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Hanjie DengSchool of Instrument Science and Engineering, Southeast University, Nanjing, China.
Yuchao HeCAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Haoshi ZhangCAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Zhenzhen LiuSurgery Division, Epilepsy Center, Shenzhen Children's Hospital, Shenzhen, China.
Shixiong ChenCAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Mingjiang WangSchool of Electronic and Information Engineering, Harbin Institute of Technology, Shenzhen, China.
Guanglin LiCAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pitch, as a sensation of the sound frequency, is a crucial attribute toward constructing a natural voice for communication. Producing intelligible sounds with normal pitches depend on substantive interdependencies among facial and neck muscles. Clarifying the interrelations between the pitches and the corresponding muscular activities would be helpful for evaluating the pitch-related phonating functions, which would play a significant role both in training pronunciation and in assessing dysphonia. In this study, the speech signals and the high-density surface electromyography (HD sEMG) signals were synchronously acquired when phonating [a:], [i:], and [ә:] vowels with increasing pitches, respectively. The HD sEMG energy maps were constructed based on the root mean square values to visualize spatiotemporal characteristics of facial and neck muscle activities. Normalized median frequency (nMF) and root-mean square (nRMS) were correspondingly extracted from the speech and sEMG recordings to quantitatively investigate the correlations between sound frequencies and myoelectric characteristics. The results showed that the frame-wise energy maps built from sEMG recordings presented that the muscle contraction strength increased monotonously across pitch-rising, with left-right symmetrical distribution for the face/neck. Furthermore, the nRMS increased at a similar rate to the nMF when there were rising pitches, and the two parameters had a significant correlation across different vowel tasks [(a:) (0.88 ± 0.04), (i:) (0.89 ± 0.04), and (ә:) (0.87 ± 0.05)]. These findings suggested the possibility of utilizing muscle contraction patterns as a reference for evaluating pitch-related phonation functions. The proposed method could open a new window for developing a clinical approach for assessing the muscular functions of dysphonia.

Indexed as

high-densityphonation functionpitchesspeech communicationsurface electromyogram

Identifiers

PMID35937895
PMCPMC9354519

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