Relevant bibliographies by topics / Computer-aided pronunciation training

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Computer-aided pronunciation training'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Computer-aided pronunciation training"

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Liang, Chaohui, and Jiling Shang. "Optimization of Computer-aided English Pronunciation Training Data Analysis System." Computer-Aided Design and Applications 18, S4 (January 13, 2021): 37–48. http://dx.doi.org/10.14733/cadaps.2021.s4.37-48.

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Cherepanova, Olga D. "Contrastive phonetic analysis of Russian and German tongue twisters." Rhema, no. 3, 2018 (2018): 119–35. http://dx.doi.org/10.31862/2500-2953-2018-3-119-135.

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This article is a review of a contrastive phonetic analysis of German and Russian tongue twisters. The aim of the survey was to examine in which way tongue twisters reflect peculiarities of a language sound system and whether it is efficient to turn to tongue twisters while improving foreign language pronunciation, e.g. to use them in computer-aided pronunciation training systems.
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Meng, Fanbo, Zhiyong Wu, Jia Jia, Helen Meng, and Lianhong Cai. "Synthesizing English emphatic speech for multimodal corrective feedback in computer-aided pronunciation training." Multimedia Tools and Applications 73, no. 1 (August 3, 2013): 463–89. http://dx.doi.org/10.1007/s11042-013-1601-y.

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Qian, Xiaojun, Helen Meng, and Frank Soong. "A Two-Pass Framework of Mispronunciation Detection and Diagnosis for Computer-Aided Pronunciation Training." IEEE/ACM Transactions on Audio, Speech, and Language Processing 24, no. 6 (June 2016): 1020–28. http://dx.doi.org/10.1109/taslp.2016.2526782.

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Agarwal, Chesta, and Pinaki Chakraborty. "A review of tools and techniques for computer aided pronunciation training (CAPT) in English." Education and Information Technologies 24, no. 6 (July 1, 2019): 3731–43. http://dx.doi.org/10.1007/s10639-019-09955-7.

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Hai, Yanfei. "Computer-aided teaching mode of oral English intelligent learning based on speech recognition and network assistance." Journal of Intelligent & Fuzzy Systems 39, no. 4 (October 21, 2020): 5749–60. http://dx.doi.org/10.3233/jifs-189052.

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The purpose of this paper is to use English specific syllables and prosodic features in spoken speech data to carry out English spoken recognition, and to explore effective methods for the design and application of English speech detection and automatic recognition systems. The method proposed by this study is a combination of SVM_FF based classifier, SVM_IER based classifier and syllable classifier. Compared with the method based on the combination of other phonological characteristics such as phonological rate, intensity, formant and energy statistics and pronunciation rate, and the syllable-based classifier based on specific syllable training, a better recognition rate is obtained. In addition, this study conducts simulation experiments on the proposed English recognition and identification method based on specific syllables and prosodic features and analyzes the experimental results. The result found that the recognition performance of the English spoken recognition system constructed by this study is significantly better than the traditional model.
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Zdaranok, Yu A. "LINGUISTIC AND ACOUSTIC RESOURCES OF THE COMPUTER-BASED SYSTEM FOR ANALYSIS AND INTERPRETATION OF SPEECH INTONATION." «System analysis and applied information science», no. 4 (February 8, 2018): 59–65. http://dx.doi.org/10.21122/2309-4923-2017-4-59-65.

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This article describes a novel approach to discriminating native and nonnative utterances based on suprasegmental features that constitute the intonation of the syntagma. In each language speech presents a diverse number of suprasegmental phenomena, which working together, create a certain coloring of spoken speech, intonationally giving out the emotional involvement or detachment of the speaker. To any language, there is a variety of intonational patterns, which were first identified as such by E. A. Bryzgunova, and are used for teaching Russian as a foreign language by I. V. Odintsova. According to the seven basic intonational patterns of E. A. Bryzgunova, BM Lobanov created the universal melodic portraits (UMP), which can be normalized in time in accordance with the position of its pre-nuclear, nuclear and bender parts. With the help of intonation, information is transmitted at the linguistic and paralinguistic level. A lot depends on correct intoning: perception, understanding and meaning. A thought covered in words with the right acoustic pronunciation gives a correct impression of things in human mind. Thus, the so-called interference while studying a foreign language is excluded. Linguistic and acoustic resources provide the basis for computer-aided intonation training in and outside the classroom. There is a great need for new and innovative computer-aided programs. But there is no necessary software available for such teaching system and, therefore, such system appears to be of great relevance. Presented work describes as well the algorithm of designing of the computer-based system for analysis and interpretation of speech intonation.
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Su, Pei-Hao, Chuan-Hsun Wu, and Lin-Shan Lee. "A Recursive Dialogue Game for Personalized Computer-Aided Pronunciation Training." IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2014, 1. http://dx.doi.org/10.1109/taslp.2014.2375572.

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Dissertations / Theses on the topic "Computer-aided pronunciation training"

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Gazdík, Peter. "Automatické hodnocení anglické výslovnosti nerodilých mluvčích." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-413332.

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Computer-Assisted Pronunciation Training (CAPT) is becoming more and more popular these days. However, the accuracy of existing CAPT systems is still quite low. Therefore, this diploma thesis focuses on improving existing methods for automatic pronunciation evaluation on the segmental level. The first part describes common techniques for this task. Afterwards, we proposed the system based on two approaches. Finally, performed experiments show significant improvement over the reference system.
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Wu, Chuan-Hsun, and 吳全勳. "Dialogue Game Considering Articulatory Features for Personalized Computer-Aided Pronunciation Training." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/27239401086296352161.

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碩士
國立臺灣大學
資訊工程學研究所
103
In this thesis we propose a new dialogue game framework considering Articulatory Features (AFs) for personalized Computer-Assisted Language Learning (CALL). We use an automatic pronunciation evaluator and a set of dialogue scripts for reastaurant scenarios, with policy for selecting learning sentence trained by Reinforcement Learning (RL), based on continuous state Markov Decision Process (MDP) as the system’s model, We utilize a corpus of real learner data, including pronunciation Error Patterns (EP) annotated by Mandarin teachers, to train a learner simulation model, in order to produce a huge quantity of simulated learners for MDP training. This thesis proposes a new concept of considering Articulatory Features (AFs) in a dialogue game for Computer-Assisted Language Learning (CALL). In the previous work, the learner has to go through longer dialogue paths (more dialogue turns) to practice some rare and ill-pronounced pronunciation units. Here the new approach is based on an important hypothesis: practicing other pronunciation unitswith highproportion of the same set of AFs of a considered rare unit, taken as ’pseudo practice’, can somehow offer improvement to the pronunciation of the considered rare unit. We further set different weights for different AFs within different pronunciation units, so as to have the system concentrated on those rare or ill-pronounced units. Experimental results verify the feasibility of the proposed framework based on the hypothesis above.
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"Towards Perceptually Enhanced Corrective Feedback Generation in Computer-Aided Pronunciation Training with Crowdsourcing and Spectral Space Warping Strategies." 2016. http://repository.lib.cuhk.edu.hk/en/item/cuhk-1292253.

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本論文對現有的計算機輔助發音訓練系統提供的各種教學反饋方法進行了調查研究，并提出了從感知方面對糾錯型反饋進行加強的建議。此論文所涉及的研究與討論是基於以中文為母語的英文學習者這一實例來進行的。改進建議包括下述兩個方面的內容：（1）基于听者的感知對系統偵測出的錯誤發音劃分優先級；以及（2）合成個人 化的正確發音以便於學習者感知與模仿。
有選擇的（而非面面俱到的）對學習者所犯錯誤進行標示在教學上具有重要作用。聽者所感知的語音清晰度是一條合理的可用來劃分錯誤優先級的標準。本論文的第一項貢獻首先使用了新穎的眾包（crowdsourcing）的方法來收集針對學習者的英文單詞發音錯誤的就其嚴重程度的主觀評分。緊接著，我們提出WorkerRank算法來對從匿名的評分者（Worker）手中收集來的眾包的數據的質量進行控制。最終，一部分評分者因其可靠性得到認可而被篩選出來，而這部分評分者所提供的評分則被認為是可靠的數據。
本論文的第二項貢獻是使用可靠的眾包數據以及音位建模（phonological modeling）來預測人類對學習者的單詞發音錯誤的主觀評級。單詞錯誤發音的評級高度依賴于以音位規則（phonological rules）來表示的音素錯誤（phonetic errors）。因此，每一條音位規則都被賦予一個評分，而這些音位規則的評分又反之用於預測單詞錯誤發音的評級。實驗結果表明，此機器預測方法與人類感知在對錯誤發音評分方面的一致性，能夠達到與人類自身評分的一致性相當的 程度。
在發音錯誤被標示出來之後，如果學習者能夠聽到對應的音色為其本人聲音的標準發音，這將會從感知的角度對學習者改正發音錯誤，提高發音水平起到非常有效的作用。本論文的第三項貢獻是提出了一種新穎的方法來達到上述目標。該方法採用頻譜空間變換（spectral space warping）的手段，在基於隱馬可夫模型（HMMbased）的語音合成應用中實現跨語言的聲音轉換。此方法所用到的數據僅為一位目標說話人（target speaker）的中文普通話錄音語料及一位參考說話人（reference speaker）的英文語料。假設任一說話人的頻譜空間都是由若干個通用的基本單位（例如 tied states）組成的。所提出的方法的目標是在兩個頻譜空間之間找出一組最優的一一映射從而使其中之一的頻譜空間向另一頻譜空間變換。這一過程將原本說話人及語言都不相同的兩組語料相互關聯了起來。因此我們最終可以合成較高質量的目標說話人的英文語音。
This thesis investigates different methodologies for feedback generation in existing computer-aided pronunciation training (CAPT) systems and proposes perceptually enhancing corrective feedback generation (in the context of CAPT systems targeting Chinese learners of English) from two aspects: (1) prioritizing detected mispronunciations based on listeners’ perception; and (2) synthesizing personalized correct pronunciations to facilitate learners’ perception.
It is of pedagogical importance that only a few (instead of all) errors made by the learner should be signaled at one time. Speech intelligibility to listeners is a reasonable criterion to prioritize errors. The first contribution of this thesis starts with collecting perceptual ratings of word-level non-native English mispronunciations according to different levels of severity using the novel crowdsourcing technique. Then,the WorkerRank algorithm is devised to control the quality of the crowdsourced data from anonymous Workers. As a result, a subset of the Workers is selected as reliable and their ratings are deemed reliable.
The second contribution focuses on the use of reliable crowdsourced data and phonological modeling to predict human perceptual gradations on non-native English word mispronunciations. Word mispronunciation gradation is highly dependent on phonetic errors which are represented by phonological rules. Hence, each phonological rule can be assigned with a gradation score which is, in turn, used to predict gradations of word mispronunciations. Experimental results show that this prediction mechanism is able to closely approximate human listeners’ perception in terms of the level of agreement on ratings of mispronunciations.
Once errors are pinpointed, it can be perceptually very useful for a learner to listen to the corresponding correct pronunciations in his/her own voice. The third contribution focuses on achieving this goal by implementing a novel spectral space warping approach to cross-lingual voice transformation for HMM-based speech synthesis. This approach uses only a target speaker’s Mandarin corpus and a reference speaker’s English corpus. Based on an assumption that the spectral space of a speaker is composed of universal elementary units (e.g. tied states) in different languages, the proposed approach warps one spectral space towards the other by finding an optimal one-to-one tied state mapping, thus bridging across both language and speaker differences between the two corpora.Consequently,the target speaker’s high-quality English speech can be synthesized.
Wang, Hao.
Thesis Ph.D. Chinese University of Hong Kong 2016.
Includes bibliographical references (leaves ).
Abstracts also in Chinese.
Title from PDF title page (viewed on …).
Detailed summary in vernacular field only.
Detailed summary in vernacular field only.
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"A computational model for studying L1’s effect on L2 speech learning." Doctoral diss., 2018. http://hdl.handle.net/2286/R.I.51589.

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abstract: Much evidence has shown that first language (L1) plays an important role in the formation of L2 phonological system during second language (L2) learning process. This combines with the fact that different L1s have distinct phonological patterns to indicate the diverse L2 speech learning outcomes for speakers from different L1 backgrounds. This dissertation hypothesizes that phonological distances between accented speech and speakers' L1 speech are also correlated with perceived accentedness, and the correlations are negative for some phonological properties. Moreover, contrastive phonological distinctions between L1s and L2 will manifest themselves in the accented speech produced by speaker from these L1s. To test the hypotheses, this study comes up with a computational model to analyze the accented speech properties in both segmental (short-term speech measurements on short-segment or phoneme level) and suprasegmental (long-term speech measurements on word, long-segment, or sentence level) feature space. The benefit of using a computational model is that it enables quantitative analysis of L1's effect on accent in terms of different phonological properties. The core parts of this computational model are feature extraction schemes to extract pronunciation and prosody representation of accented speech based on existing techniques in speech processing field. Correlation analysis on both segmental and suprasegmental feature space is conducted to look into the relationship between acoustic measurements related to L1s and perceived accentedness across several L1s. Multiple regression analysis is employed to investigate how the L1's effect impacts the perception of foreign accent, and how accented speech produced by speakers from different L1s behaves distinctly on segmental and suprasegmental feature spaces. Results unveil the potential application of the methodology in this study to provide quantitative analysis of accented speech, and extend current studies in L2 speech learning theory to large scale. Practically, this study further shows that the computational model proposed in this study can benefit automatic accentedness evaluation system by adding features related to speakers' L1s.
Dissertation/Thesis
Doctoral Dissertation Speech and Hearing Science 2018
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Book chapters on the topic "Computer-aided pronunciation training"

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Kröger, Bernd J., Peter Birkholz, Rüdiger Hoffmann, and Helen Meng. "Audiovisual Tools for Phonetic and Articulatory Visualization in Computer-Aided Pronunciation Training." In Development of Multimodal Interfaces: Active Listening and Synchrony, 337–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12397-9_29.

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Conference papers on the topic "Computer-aided pronunciation training"

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Qin, Yi, and Guonian Wang. "A computer-aided Chinese pronunciation training program for English-speaking learners." In 2014 International Conference on Asian Language Processing (IALP). IEEE, 2014. http://dx.doi.org/10.1109/ialp.2014.6973499.

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Yuen, Ka-Wa, Wai-Kim Leung, Peng-fei Liu, Ka-Ho Wong, Xiao-jun Qian, Wai-Kit Lo, and Helen Meng. "Enunciate: An internet-accessible computer-aided pronunciation training system and related user evaluations." In 2011 Oriental COCOSDA 2011 - International Conference on Speech Database and Assessments. IEEE, 2011. http://dx.doi.org/10.1109/icsda.2011.6085985.

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Ning, Yishuang, Zhiyong Wu, Jia Jia, Fanbo Meng, Helen Meng, and Lianhong Cai. "HMM-based emphatic speech synthesis for corrective feedback in computer-aided pronunciation training." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178909.

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Wang, Lan, Xin Feng, and Helen M. Meng. "Automatic generation and pruning of phonetic mispronunciations to support computer-aided pronunciation training." In Interspeech 2008. ISCA: ISCA, 2008. http://dx.doi.org/10.21437/interspeech.2008-466.

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Qian, Xiaojun, Helen Meng, and Frank Soong. "A two-pass framework of mispronunciation detection & diagnosis for computer-aided pronunciation training." In 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). IEEE, 2015. http://dx.doi.org/10.1109/apsipa.2015.7415299.

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Qian, Xiaojun, Helen Meng, and Frank Soong. "Capturing L2 segmental mispronunciations with joint-sequence models in Computer-Aided Pronunciation Training (CAPT)." In 2010 7th International Symposium on Chinese Spoken Language Processing (ISCSLP). IEEE, 2010. http://dx.doi.org/10.1109/iscslp.2010.5684845.

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Bu, Yaohua, Tianyi Ma, Weijun Li, Hang Zhou, Jia Jia, Shengqi Chen, Kaiyuan Xu, et al. "PTeacher: a Computer-Aided Personalized Pronunciation Training System with Exaggerated Audio-Visual Corrective Feedback." In CHI '21: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3411764.3445490.

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Qian, Xiaojun, Helen Meng, and Frank K. Soong. "On mispronunciation lexicon generation using joint-sequence multigrams in computer-aided pronunciation training (CAPT)." In Interspeech 2011. ISCA: ISCA, 2011. http://dx.doi.org/10.21437/interspeech.2011-330.

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Indrayanti, Linda, Tsuyoshi Usagawa, Yoshifumi Chisaki, and Titon Dutono. "Evaluation of Pronunciation by means of Automatic Speech Recognition System for Computer Aided Indonesian Language Learning." In 2006 7th International Conference on Information Technology Based Higher Education and Training. IEEE, 2006. http://dx.doi.org/10.1109/ithet.2006.339812.

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Qian, Xiaojun, Frank K. Soong, and Helen Meng. "Discriminative acoustic model for improving mispronunciation detection and diagnosis in computer-aided pronunciation training (CAPT)." In Interspeech 2010. ISCA: ISCA, 2010. http://dx.doi.org/10.21437/interspeech.2010-278.

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