Relevant bibliographies by topics / Automatic pronunciation evaluation

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

Academic literature on the topic 'Automatic pronunciation evaluation'

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

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Journal articles on the topic "Automatic pronunciation evaluation"

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Kyuchul Yoon. "Automatic Evaluation Model of English Pronunciation." Studies in Linguistics ll, no. 19 (2011): 149–68. http://dx.doi.org/10.17002/sil..19.201104.149.

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Xibin Jia, Hua Du, Yanfang Han, Kewei Zhang, and David Powers. "Audio/Visual Speech based Pronunciation Automatic Evaluation." International Journal of Intelligent Information Processing 4, no. 1 (2013): 98–104. http://dx.doi.org/10.4156/ijiip.vol4.issue1.11.

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Bargh, John A., Shelly Chaiken, Paula Raymond, and Charles Hymes. "The Automatic Evaluation Effect: Unconditional Automatic Attitude Activation with a Pronunciation Task." Journal of Experimental Social Psychology 32, no. 1 (1996): 104–28. http://dx.doi.org/10.1006/jesp.1996.0005.

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Moustroufas, N., and V. Digalakis. "Automatic pronunciation evaluation of foreign speakers using unknown text." Computer Speech & Language 21, no. 1 (2007): 219–30. http://dx.doi.org/10.1016/j.csl.2006.04.001.

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Park, Hye-bin, Dong Heon Kim, and Jinoo Joung. "An automatic pronunciation evaluation system using non-native teacher's speech model." Journal of the Institute of Internet Broadcasting and Communication 16, no. 2 (2016): 131–36. http://dx.doi.org/10.7236/jiibc.2016.16.2.131.

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HINCKS, REBECCA. "Speech technologies for pronunciation feedback and evaluation." ReCALL 15, no. 1 (2003): 3–20. http://dx.doi.org/10.1017/s0958344003000211.

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Educators and researchers in the acquisition of L2 phonology have called for empirical assessment of the progress students make after using new methods for learning (Chun, 1998, Morley, 1991). The present study investigated whether unlimited access to a speech-recognition-based language-learning program would improve the general standard of pronunciation of a group of middle-aged immigrant professionals studying English in Sweden. Eleven students were given a copy of the program Talk to Me from Auralog as a supplement to a 200-hour course in Technical English, and were encouraged to practise on their home computers. Their development in spoken English was compared with a control group of fifteen students who did not use the program. The program is evaluated in this paper according to Chapelle’s (2001) six criteria for CALL assessment. Since objective human ratings of pronunciation are costly and can be unreliable, our students were pre- and post-tested with the automatic PhonePass SET-10 test from Ordinate Corp. Results indicate that practice with the program was beneficial to those students who began the course with a strong foreign accent but was of limited value for students who began the course with better pronunciation. The paper begins with an overview of the state of the art of using speech recognition in L2 applications.
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Song, Yin, and Weiqian Liang. "Experimental study of discriminative adaptive training and MLLR for automatic pronunciation evaluation." Tsinghua Science and Technology 16, no. 2 (2011): 189–93. http://dx.doi.org/10.1016/s1007-0214(11)70029-3.

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Wang, Yongjuan, and Peng Zhao. "A Probe into Spoken English Recognition in English Education Based on Computer-Aided Comprehensive Analysis." International Journal of Emerging Technologies in Learning (iJET) 15, no. 03 (2020): 223. http://dx.doi.org/10.3991/ijet.v15i03.12937.

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At present, computer-aided spoken English learning is becoming increasingly popular among learners. The computer-aided comprehensive analysis tech-nology can evaluate and correct learner's spoken pronunciation, thereby im-proving their pronunciation. Based on computer-aided comprehensive analy-sis, this paper aims to explore the automatic recognition and scoring methods of spoken English in English education. For this, it studies the effective matching of the feedback information with the known pronunciation scoring results, and then develops a computer evaluation plug-in consisting of dif-ferent modules such as user login, English spoken speech acquisition and recognition, voice evaluation, speech broadcast, and spoken dialogue. The research results show that the computer evaluation plug-in matches and compares the extracted feature parameters of input speech with the standard features, scores the spoken language input by the learner, and gives the cor-rect pronunciation so that the learner can get feedback in time. For different stages of English learning, the focus of recognition technology and the spo-ken recognition algorithms applied also vary. The research findings provide theoretical and technical support for oral English recognition, error correction and scoring.
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Sato, Kei, Masaharu Kato, and Tetsuo Kosaka. "An investigation of vowel substitution rules in the automatic evaluation system of English pronunciation." Journal of the Acoustical Society of America 133, no. 5 (2013): 3247. http://dx.doi.org/10.1121/1.4805215.

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Wang, Zhongbo, and Qi Wu. "Research on automatic evaluation method of Mandarin Chinese pronunciation based on 5G network and FPGA." Microprocessors and Microsystems 80 (February 2021): 103534. http://dx.doi.org/10.1016/j.micpro.2020.103534.

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Dissertations / Theses on the topic "Automatic pronunciation evaluation"

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Almeida, Gustavo Augusto de Mendonça. "Using phonetic knowledge in tools and resources for Natural Language Processing and Pronunciation Evaluation." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-17112016-161826/.

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This thesis presents tools and resources for the development of applications in Natural Language Processing and Pronunciation Training. There are four main contributions. First, a hybrid grapheme-to-phoneme converter for Brazilian Portuguese, named Aeiouadô, which makes use of both manual transcription rules and Classification and Regression Trees (CART) to infer the phone transcription. Second, a spelling correction system based on machine learning, which uses the trascriptions produced by Aeiouadô and is capable of handling phonologically-motivated errors, as well as contextual errors. Third, a method for the extraction of phonetically-rich sentences, which is based on greedy algorithms. Fourth, a prototype system for automatic pronunciation assessment, especially designed for Brazilian-accented English.<br>Esta dissertação apresenta recursos voltados para o desenvolvimento de aplicações de reconhecimento de fala e avaliação de pronúncia. São quatro as contribuições aqui discutidas. Primeiro, um conversor grafema-fonema híbrido para o Português Brasileiro, chamado Aeiouadô, o qual utiliza regras de transcrição fonética e Classification and Regression Trees (CART) para inferir os fones da fala. Segundo, uma ferramenta de correção automática baseada em aprendizado de máquina, que leva em conta erros de digitação de origem fonética, que é capaz de lidar com erros contextuais e emprega as transcrições geradas pelo Aeiouadô. Terceiro, um método para a extração de sentenças foneticamente-ricas, tendo em vista a criação de corpora de fala, baseado em algoritmos gulosos. Quarto, um protótipo de um sistema de reconhecimento e correção de fala não-nativa, voltado para o Inglês falado por aprendizes brasileiros.
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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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Lai, Tzu-Ting, and 賴子婷. "The Research of Automatic Pronunciation Evaluation for Beginners." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/83928081899689468539.

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碩士<br>國立臺灣師範大學<br>資訊工程學系<br>103<br>“Computer Assisted Pronunciation Training “program is primary designed to assist students in language learning. The program provides the feedback based on each individual need and it helps beginners to repeat practice proper pronunciation. The research utilizes the speech recognition and string matching to build speech recognize model for beginners to practice pronunciation. Research consisted two main parts. First part is to build speech recognize model, which is to record JTES corpus. The next step is to select the top speeches in JTJS corpus to do model adaption. The second part is to evaluate speeches by using string-matching method. We proposal Levenshtein Distance-Like approach and using cubic polynomial fit to find threshold. Those approaches help us to separate into four levels of the evaluating standards (excellent, average, inferior, and re-recording). The result from the experiment shows the accuracy of evaluating process is around 75% when the program is separated into four levels. This is supported by both human and systematic evaluation. Based on the analysis of Pearson correlation, correlation between human and system evaluation is 0.71, which mean two variables are correlated. Therefore, the system is credible for beginners to learn and enhance their verbal skill.
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Song, Zhan-Yan, and 宋展延. "Development of Automatic Evaluation Systems on Taiwanese Elementary School Students’ English Pronunciation." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/n3kers.

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碩士<br>國立清華大學<br>電機工程學系<br>107<br>The motivation behind this thesis is to help elementary school students from rural areas to master English pronunciation. We explore several methods to judge whether they pronounced precisely. English words were collected from school children in New Taipei City, and scoring rules were defined by their teachers. Then, we try to carry out an automatic evaluation system applicable to different words. The goal is to compare the audio files from the teachers with those from the pupils and to find out the difference among them corresponding to different word characteristics. The features which are extracted from these audio signals include the short-term log root mean square energy, the rate of rise of log root mean square energy, the zero crossing rate, the length of a speech sound, the Mel-scale frequency cepstral coefficients, and the trajectory of first two formants. Subsequently, the boxplots are adopted to make a statistical analysis and to observe the distribution of the audio data with different scores. In addition, a support vector machine is used to evaluate the vowel segment of the words. The Mel-scale frequency cepstral coefficients, the linear predictive coefficients and the volume are taken as the characteristics. Data from 80% of the students are used as the training set to build the model, while the rest of them are treated as testing data. The score prediction accuracy was about 64% for the vowel segment. Thus, we have an evaluation system which consists of the boxplot and the SVM accuracy. Also, a recognition system is developed to distinguish whether the students pronounce another word or not. We combine both systems to realize the overall evaluation system. In the future, we hope students can use the system and thus obtain some feedbacks to check which parts of the word are not well pronounced, and practice the correct pronunciation with the assistance of this evaluation system.
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Book chapters on the topic "Automatic pronunciation evaluation"

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Liu, Xiaobin, Chunxiao Zhu, Jianli Jiao, and Manfei Xu. "Promoting English Pronunciation via Mobile Devices-Based Automatic Speech Evaluation (ASE) Technology." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94505-7_27.

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Darģis, Roberts, Normunds Grūzītis, Ilze Auzin̦a, and Kaspars Stepanovs. "Creation of Language Resources for the Development of a Medical Speech Recognition System for Latvian." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2020. http://dx.doi.org/10.3233/faia200615.

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This paper describes an ongoing work on the creation of Latvian language resources for the medical domain focusing on digital imaging to develop a medical speech recognition system for Latvian. The language resources include a pronunciation lexicon, a text corpus for language modelling, and an orthographically transcribed speech corpus for the (i) adaptation of the acoustic model, (ii) evaluation of the speech recognition accuracy, (iii) development and testing of rewrite rules for automatic text conversion to the spoken form and back to the written form. This work is part of a larger industry-driven research project which aims at the development of specific Latvian speech recognition systems for the medical domain.
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Conference papers on the topic "Automatic pronunciation evaluation"

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Gupta, Chitralekha, Haizhou Li, and Ye Wang. "Automatic Pronunciation Evaluation of Singing." In Interspeech 2018. ISCA, 2018. http://dx.doi.org/10.21437/interspeech.2018-1267.

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Yin Song, Weiqian Liang, and Runsheng Liu. "Lattice-based GOP in automatic pronunciation evaluation." In 2nd International Conference on Computer and Automation Engineering (ICCAE 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccae.2010.5451568.

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Yin Song, Weiqian Liang, and Runsheng Liu. "Lattice-based GOP in automatic pronunciation evaluation." In 2nd International Conference on Computer and Automation Engineering (ICCAE 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccae.2010.5451809.

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Yi, He, Xiao Yunpeng, Zhao Xinru, Qi Xin, Xu Xiaoying, and Ye Weiping. "The improvements on automatic mandarin pronunciation evaluation." In 2012 2nd International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2012. http://dx.doi.org/10.1109/iccsnt.2012.6525985.

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Tepperman, J., and S. Narayanan. "Hidden-articulator Markov models for pronunciation evaluation." In IEEE Workshop on Automatic Speech Recognition and Understanding, 2005. IEEE, 2005. http://dx.doi.org/10.1109/asru.2005.1566471.

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Xu, Shuang, Dengfeng Ke, Jie Jiang, Xi Yang, Hongyan Li, and Bo Xu. "Automatic Pronunciation Evaluation Based on Feature Extraction and Combination." In 2008 3rd International Conference on Innovative Computing Information and Control. IEEE, 2008. http://dx.doi.org/10.1109/icicic.2008.179.

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Zheng, Jing, Chao Huang, Min Chu, Frank K. Soong, and Wei-ping Ye. "Generalized Segment Posterior Probability for Automatic Mandarin Pronunciation Evaluation." In 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.367198.

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Luo, Dean, Naoya Shimomura, Nobuaki Minematsu, Yutaka Yamauchi, and Keikichi Hirose. "Automatic pronunciation evaluation of language learners' utterances generated through shadowing." In Interspeech 2008. ISCA, 2008. http://dx.doi.org/10.21437/interspeech.2008-476.

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Mathad, Vikram C., Tristan J. Mahr, Nancy Scherer, et al. "The Impact of Forced-Alignment Errors on Automatic Pronunciation Evaluation." In Interspeech 2021. ISCA, 2021. http://dx.doi.org/10.21437/interspeech.2021-1403.

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Ke Yan. "Phone-dependent transformation of posterior probability measure for automatic pronunciation quality evaluation." In 2014 IEEE Workshop on Electronics, Computer and Applications (IWECA). IEEE, 2014. http://dx.doi.org/10.1109/iweca.2014.6845702.

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