Workshop on Speech and Language Processing for Assistive Technologies最新文献

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Predicting Intelligible Speaking Rate in Individuals with Amyotrophic Lateral Sclerosis from a Small Number of Speech Acoustic and Articulatory Samples. 从少量语音声学和发音样本预测肌萎缩侧索硬化症患者的可理解说话率

Workshop on Speech and Language Processing for Assistive Technologies Pub Date : 2016-09-01 DOI: 10.21437/SLPAT.2016-16

Jun Wang, Prasanna V Kothalkar, Myungjong Kim, Yana Yunusova, Thomas F Campbell, Daragh Heitzman, Jordan R Green

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引用次数: 0

Recognizing Whispered Speech Produced by an Individual with Surgically Reconstructed Larynx Using Articulatory Movement Data. 利用关节运动数据识别手术重建喉部患者的低声语音。

Workshop on Speech and Language Processing for Assistive Technologies Pub Date : 2016-09-01 DOI: 10.21437/SLPAT.2016-14

Beiming Cao, Myungjong Kim, Ted Mau, Jun Wang

{"title":"Recognizing Whispered Speech Produced by an Individual with Surgically Reconstructed Larynx Using Articulatory Movement Data.","authors":"Beiming Cao, Myungjong Kim, Ted Mau, Jun Wang","doi":"10.21437/SLPAT.2016-14","DOIUrl":"https://doi.org/10.21437/SLPAT.2016-14","url":null,"abstract":"Individuals with larynx (vocal folds) impaired have problems in controlling their glottal vibration, producing whispered speech with extreme hoarseness. Standard automatic speech recognition using only acoustic cues is typically ineffective for whispered speech because the corresponding spectral characteristics are distorted. Articulatory cues such as the tongue and lip motion may help in recognizing whispered speech since articulatory motion patterns are generally not affected. In this paper, we investigated whispered speech recognition for patients with reconstructed larynx using articulatory movement data. A data set with both acoustic and articulatory motion data was collected from a patient with surgically reconstructed larynx using an electromagnetic articulograph. Two speech recognition systems, Gaussian mixture model-hidden Markov model (GMM-HMM) and deep neural network-HMM (DNN-HMM), were used in the experiments. Experimental results showed adding either tongue or lip motion data to acoustic features such as mel-frequency cepstral coefficient (MFCC) significantly reduced the phone error rates on both speech recognition systems. Adding both tongue and lip data achieved the best performance.","PeriodicalId":92315,"journal":{"name":"Workshop on Speech and Language Processing for Assistive Technologies","volume":"2016 ","pages":"80-86"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5800526/pdf/nihms885504.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35813726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

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