The Role of Voice Quality in Mandarin Sarcastic Speech: An Acoustic and Electroglottographic Study

Li, Shanpeng; Gu, Wentao; Liu, Lei; Tang, Ping

doi:10.1044/2020_jslhr-19-00166

Cited by 12 publications

(2 citation statements)

References 45 publications

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“…The literature [18] proposed the recursive multilevel fusion network (RMFN), which decomposes the spatio-temporal fusion problem into multiple stages, each focusing on a subset of multimodal signals for specialized and efficient fusion. The literature [19] proposes a multimodal information fuzzy fusion algorithm. The identification of gait phases was accomplished using multimodal sensory data using a fuzzy fusion algorithm.…”

Section: Related Workmentioning

confidence: 99%

English Pronunciation Standards Based on Multimodal Acoustic Sensors

Zhu

2021

Journal of Sensors

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In recent years, economic globalization is the trend, and communication between countries is getting closer and closer; more and more people begin to pay attention to learning spoken English. The development of computer-aided language learning makes it more convenient for people to learn spoken English; however, the detection and correction of incorrect English pronunciation, which is its core, are still inadequate. In this paper, we propose a multimodal end-to-end English pronunciation error detection and correction model based on audio and video, which does not require phoneme forced alignment of the English pronunciation video signal to be processed, and uses rich audio and video features for English pronunciation error detection, which improves the error detection accuracy to a great extent especially in noisy environments. To address the shortcomings of the current lip feature extraction algorithm which is too complicated and not enough characterization ability, a feature extraction scheme based on the lip opening and closing angle is proposed. The lip syllable frames are obtained by video frame splitting, the syllables are denoised, the key point information of the lips is obtained using a gradient enhancement-based regression tree algorithm, the effects of speaker tilt and movement are removed by scale normalization, and finally, the lip opening and closing angles are calculated using mathematical geometry, and the lip feature values are generated by combining the angle changes.

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Section: Related Workmentioning

confidence: 99%

English Pronunciation Standards Based on Multimodal Acoustic Sensors

Zhu

2021

Journal of Sensors

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“…In addition to indexical information, differences in voice quality can signal lexical contrasts (the difference between (glottalized) /t/ and (nonglottalized) /d/ in some American English varieties [5], phonemic contrasts of vowels in Gujarati [6], cf. [7]), illocution type (breathier voice in rhetorical than in information-seeking questions [8,9]), irony and sarcasm [10,11], as well as information-structure [e.g., 12 on Finnish focus marking]. Voice quality also plays a role in signalling emotions [13]; as a social marker for gender identity [14 for English, 15 for Japanese] and often occurs in infant-directed speech (IDS) [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Testing Acoustic Voice Quality Classification Across Languages and Speech Styles

Braun¹,

Dehé²,

Einfeldt³

et al. 2021

Interspeech 2021

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Many studies relate acoustic voice quality measures to perceptual classification. We extend this line of research by training a classifier on a balanced set of perceptually annotated voice quality categories with high inter-rater agreement, and test it on speech samples from a different language and on a different speech style. Annotations were done on continuous speech from different laboratory settings. In Experiment 1, we trained a random forest with Standard Chinese and German recordings labelled as modal, breathy, or glottalized. The model had an accuracy of 78.7% on unseen data from the same sample (most important variables were harmonics-to-noise ratio, cepstral-peak prominence, and H1-A2). This model was then used to classify data from a different language (Icelandic, Experiment 2) and to classify a different speech style (German infant-directed speech (IDS), Experiment 3). Cross-linguistic generalizability was high for Icelandic (78.6% accuracy), but lower for German IDS (71.7% accuracy). Accuracy of recordings of adult-directed speech from the same speakers as in Experiment 3 (77%, Experiment 4) suggests that it is the special speech style of IDS, rather than the recording setting that led to lower performance. Results are discussed in terms of efficiency of coding and generalizability across languages and speech styles.

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