Relevant bibliographies by topics / Pronunciation adaptation

Contents

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

Academic literature on the topic 'Pronunciation adaptation'

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

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Journal articles on the topic "Pronunciation adaptation"

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Goronzy, Silke, Stefan Rapp, and Ralf Kompe. "Generating non-native pronunciation variants for lexicon adaptation." Speech Communication 42, no. 1 (January 2004): 109–23. http://dx.doi.org/10.1016/j.specom.2003.09.003.

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Kaneko, Emiko, Younghyon Heo, Gregory Iverson, and Ian Wilson. "Quasi-neutralization in the acquisition of English coronal fricatives by native speakers of Japanese." Journal of Second Language Pronunciation 1, no. 1 (March 30, 2015): 65–85. http://dx.doi.org/10.1075/jslp.1.1.03kan.

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Second language learners show various forms of mispronunciation, or modification, of target pronunciation, most perhaps due to direct native language transfer, but others, summarized here, to deflected contrast, hypercorrection and covert contrast. The present paper reports on a novel form of adaptation that we term ‘quasi-neutralization,’ in which acoustic characteristics of competing target phonemes are found within the same interlanguage segment (e.g., think [θɪŋk] pronounced as [θsiŋk]). The three English voiceless coronal fricatives /s/, /ʃ/, /θ/ were elicited from Japanese learners of English via two techniques: a wordlist reading task that encouraged participants to focus on their pronunciation, and a sentence construction task that diverted their attention from pronunciation. Among different types of modification, quasi-neutralization was observed predominantly when participants were conscious of their pronunciation, which could reflect their linguistic insecurity as learners. This research thus illuminates another of the strategies that learners employ in the acquisition of L2 pronunciation.
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Chen, Hsueh Chu. "In-service Teachers’ Intelligibility and Pronunciation Adjustment Strategies in English Language Classrooms." English Language Teaching 9, no. 4 (February 29, 2016): 30. http://dx.doi.org/10.5539/elt.v9n4p30.

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<p>A realistic goal of pronunciation teaching in the second language context is to acquire comfortably intelligible rather than native-like pronunciation. To establish a set of teaching and learning priorities necessary for English teachers and students whose first language is Chinese, the purposes of this study are three fold: (1) Identify the pronunciation aspects that are crucial for intelligible pronunciation in actual second language (L2) Hong Kong (HK) and foreign language mainland (ML) China classrooms from in-service teachers’ points of view; (2) Investigate how teachers help their students successfully understand English classroom input through teachers’ self-reflection on which aspects of their own pronunciation they modify and adapt to make classroom discourse intelligible to students; and (3) explore the most frequently taught pronunciation aspects and the most frequently used pronunciation teaching strategies used by teachers to teach pronunciation in English classrooms. Forty-seven questionnaires were collected and analysed from in-service teachers in primary schools. Four teachers were invited to attend follow-up interviews. In order to further investigate the application of adaptation strategies and pronunciation teaching strategies in real classroom settings, eight classroom videos were collected. The data were triangulated allowing for cross checking.<strong> </strong>The findings will not only help frontline teachers become self-aware of their own pronunciation, rectify students’ recurrent difficulties in using phonological features, and improve mutual intelligibility in English language classrooms but also help explore the ways to integrate phonology courses and pronunciation teaching in second/foreign language teaching and teacher education.</p>
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LESTARI, Dessi Puji, and Sadaoki FURUI. "Adaptation to Pronunciation Variations in Indonesian Spoken Query-Based Information Retrieval." IEICE Transactions on Information and Systems E93-D, no. 9 (2010): 2388–96. http://dx.doi.org/10.1587/transinf.e93.d.2388.

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Lee, Damheo, Donghyun Kim, Seung Yun, and Sanghun Kim. "Phonetic Variation Modeling and a Language Model Adaptation for Korean English Code-Switching Speech Recognition." Applied Sciences 11, no. 6 (March 23, 2021): 2866. http://dx.doi.org/10.3390/app11062866.

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In this paper, we propose a new method for code-switching (CS) automatic speech recognition (ASR) in Korean. First, the phonetic variations in English pronunciation spoken by Korean speakers should be considered. Thus, we tried to find a unified pronunciation model based on phonetic knowledge and deep learning. Second, we extracted the CS sentences semantically similar to the target domain and then applied the language model (LM) adaptation to solve the biased modeling toward Korean due to the imbalanced training data. In this experiment, training data were AI Hub (1033 h) in Korean and Librispeech (960 h) in English. As a result, when compared to the baseline, the proposed method improved the error reduction rate (ERR) by up to 11.6% with phonetic variant modeling and by 17.3% when semantically similar sentences were applied to the LM adaptation. If we considered only English words, the word correction rate improved up to 24.2% compared to that of the baseline. The proposed method seems to be very effective in CS speech recognition.
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OH, Yoo Rhee, and Hong Kook KIM. "A Hybrid Acoustic and Pronunciation Model Adaptation Approach for Non-native Speech Recognition." IEICE Transactions on Information and Systems E93-D, no. 9 (2010): 2379–87. http://dx.doi.org/10.1587/transinf.e93.d.2379.

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Oh, Yoo Rhee, Jae Sam Yoon, and Hong Kook Kim. "Acoustic model adaptation based on pronunciation variability analysis for non-native speech recognition." Speech Communication 49, no. 1 (January 2007): 59–70. http://dx.doi.org/10.1016/j.specom.2006.10.006.

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Metcalf, George J. "Translation Pronunciation: A Note on Adaptation of Foreign Surnames in the United States." Names 33, no. 4 (December 1985): 268–70. http://dx.doi.org/10.1179/nam.1985.33.4.268.

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Schiel, Florian. "A new approach to speaker adaptation by modelling pronunciation in automatic speech recognition." Speech Communication 13, no. 3-4 (December 1993): 281–86. http://dx.doi.org/10.1016/0167-6393(93)90026-h.

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Duběda, Tomáš. "The Phonology of Anglicisms in French, German and Czech: A Contrastive Approach." Journal of Language Contact 13, no. 2 (December 11, 2020): 327–50. http://dx.doi.org/10.1163/19552629-01302003.

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Abstract In this article, I analyse the phonological adaptation of Anglicisms in three languages (French, German and Czech) from a contrastive perspective. The classification of standard phonological forms, based on a system of eight adaptation principles, aims at capturing the degree of phonological permeability/resistance for each of the languages. Phonological approximation (the substitution of foreign phonemes with native ones) seems to be the fundamental principle in all three languages analysed. The spelling pronunciation principle is observed predominantly in French; phonological import occurs only in German. Globally, phonological resistance increases in the following order: German – Czech – French.
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Dissertations / Theses on the topic "Pronunciation adaptation"

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Qader, Raheel. "Pronunciation and disfluency modeling for expressive speech synthesis." Thesis, Rennes 1, 2017. http://www.theses.fr/2017REN1S076/document.

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Dans la première partie de cette thèse, nous présentons une nouvelle méthode de production de variantes de prononciations qui adapte des prononciations standards, c'est-à-dire issues d'un dictionnaire, à un style spontané. Cette méthode utilise une vaste gamme d'informations linguistiques, articulatoires et acoustiques, ainsi qu'un cadre probabiliste d'apprentissage automatique, à savoir les champs aléatoires conditionnels (CAC) et les modèles de langage. Nos expériences poussées sur le corpus Buckeye démontrent l'efficacité de l'approche à travers des évaluations objectives et perceptives. Des tests d'écoutes sur de la parole synthétisée montrent que les prononciations adaptées sont jugées plus spontanées que les prononciations standards, et même que celle réalisées par les locuteurs du corpus étudié. Par ailleurs, nous montrons que notre méthode peut être étendue à d'autres tâches d'adaptation, par exemple pour résoudre des problèmes d'incohérences entre les différentes séquences de phonèmes manipulées par un système de synthèse. La seconde partie de la thèse explore une nouvelle approche de production automatique de disfluences dans les énoncés en entrée d'un système de synthèse de la parole. L'approche proposée offre l'avantage de considérer plusieurs types de disfluences, à savoir des pauses, des répétitions et des révisions. Pour cela, nous présentons une formalisation novatrice du processus de production de disfluences à travers un mécanisme de composition de ces disfluences. Nous présentons une première implémentation de notre processus, elle aussi fondée sur des CAC et des modèles de langage, puis conduisons des évaluations objectives et perceptives. Celles-ci nous permettent de conclure à la bonne fonctionnalité de notre proposition et d'en discuter les pistes principales d'amélioration
In numerous domains, the usage of synthetic speech is conditioned upon the ability of speech synthesis systems to generate natural and expressive speech. In this frame, we address the problem of expressivity in TTS by incorporating two phenomena with a high impact on speech: pronunciation variants and speech disfluencies. In the first part of this thesis, we present a new pronunciation variant generation method which works by adapting standard i.e., dictionary-based, pronunciations to a spontaneous style. Its strength and originality lie in exploiting a wide range of linguistic, articulatory and acoustic features and to use a probabilistic machine learning framework, namely conditional random fields (CRFs) and language models. Extensive experiments on the Buckeye corpus demonstrate the effectiveness of this approach through objective and subjective evaluations. Listening tests on synthetic speech show that adapted pronunciations are judged as more spontaneous than standard ones, as well as those realized by real speakers. Furthermore, we show that the method can be extended to other adaptation tasks, for instance, to solve the problem of inconsistency between phoneme sequences handled in TTS systems. The second part of this thesis explores a novel approach to automatic generation of speech disfluencies for TTS. Speech disfluencies are one of the most pervasive phenomena in spontaneous speech, therefore being able to automatically generate them is crucial to have more expressive synthetic speech. The proposed approach provides the advantage of generating several types of disfluencies: pauses, repetitions and revisions. To achieve this task, we formalize the problem as a theoretical process, where transformation functions are iteratively composed. We present a first implementation of the proposed process using CRFs and language models, before conducting objective and perceptual evaluations. These experiments lead to the conclusion that our proposition is effective to generate disfluencies, and highlights perspectives for future improvements
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Martirosian, Olga Meruzhanovna. "Adapting a pronunciation dictionary to Standard South African English for automatic speech recognition / Olga Meruzhanovna Martirosian." Thesis, North-West University, 2009. http://hdl.handle.net/10394/4902.

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The pronunciation dictionary is a key resource required during the development of an automatic speech recognition (ASR) system. In this thesis, we adapt a British English pronunciation dictionary to Standard South African English (SSAE), as a case study in dialect adaptation. Our investigation leads us in three different directions: dictionary verification, phoneme redundancy evaluation and phoneme adaptation. A pronunciation dictionary should be verified for correctness before its implementation in experiments or applications. However, employing a human to verify a full pronunciation dictionary is an indulgent process which cannot always be accommodated. In our dictionary verification research we attempt to reduce the human effort required in the verification of a pronunciation dictionary by implementing automatic and semi-automatic techniques that find and isolate possible erroneous entries in the dictionary. We identify a number of new techniques that are very efficient in identifying errors, and apply them to a public domain British English pronunciation dictionary. Investigating phoneme redundancy involves looking into the possibility that not all phoneme distinctions are required in SSAE, and investigating different methods of analysing these distinctions. The methods that are investigated include both data driven and knowledge based pronunciation suggestions for a pronunciation dictionary used in an automatic speech recognition (ASR) system. This investigation facilitates a deeper linguistic insight into the pronunciation of phonemes in SSAE. Finally, we investigate phoneme adaptation by adapting the KIT phoneme between two dialects of English through the implementation of a set of adaptation rules. Adaptation rules are extracted from literature but also formulated through an investigation of the linguistic phenomena in the data. We achieve a 93% predictive accuracy, which is significantly higher than the 71 % achievable through the implementation of previously identified rules. The adaptation of a British pronunciation dictionary to SSAE represents the final step of developing a SSAE pronunciation dictionary, which is the aim of this thesis. In addition, an ASR system utilising the dictionary is developed, achieving an unconstrained phoneme accuracy of 79.7%.
Thesis (M.Ing. (Computer Engineering))--North-West University, Potchefstroom Campus, 2009.
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Amdal, Ingunn. "Learning pronunciation variation : A data-driven approach to rule-based lecxicon adaptation for automatic speech recognition." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, 2002. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-1560.

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To achieve a robust system the variation seen for different speaking styles must be handled. An investigation of standard automatic speech recognition techniques for different speaking styles showed that lexical modelling using general-purpose variants gave small improvements, but the errors differed compared with using only one canonical pronunciation per word. Modelling the variation using the acoustic models (using context dependency and/or speaker dependent adaptation) gave a significant improvement, but the resulting performance for non-native and spontaneous speech was still far from read speech.

In this dissertation a complete data-driven approach to rule-based lexicon adaptation is presented, where the effect of the acoustic models is incorporated in the rule pruning metric. Reference and alternative transcriptions were aligned by dynamic programming, but with a data-driven method to derive the phone-to-phone substitution costs. The costs were based on the statistical co-occurrence of phones, association strength. Rules for pronunciation variation were derived from this alignment. The rules were pruned using a new metric based on acoustic log likelihood. Well trained acoustic models are capable of modelling much of the variation seen, and using the acoustic log likelihood to assess the pronunciation rules prevents the lexical modelling from adding variation already accounted for as shown for direct pronunciation variation modelling.

For the non-native task data-driven pronunciation modelling by learning pronunciation rules gave a significant performance gain. Acoustic log likelihood rule pruning performed better than rule probability pruning.

For spontaneous dictation the pronunciation variation experiments did not improve the performance. The answer to how to better model the variation for spontaneous speech seems to lie neither in the acoustical nor the lexical modelling. The main differences between read and spontaneous speech are the grammar used and disfluencies like restarts and long pauses. The language model may thus be the best starting point for more research to achieve better performance for this speaking style.

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Lin, Tai-Ming, and 林泰名. "Pronunciation Variation Modeling and Analysis Including Integration with Speaker Adaptation Techniques for Mandarin Broadcast News." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/65560022060084186925.

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Tomíčková, Markéta. "Fonetická analýza anglicismů ve francouzštině." Master's thesis, 2014. http://www.nusl.cz/ntk/nusl-338219.

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The aim of this thesis is to examine the integration of anglicisms into French from a phonetic point of view. The theoretical part deals with the delimitation of the term 'anglicism', its place in the French lexis, a comparison of the phonological systems of both languages and the regularities in phonetic adaptation of anglicisms in the target language. The practical part includes an analysis of the pronunciation of chosen anglicisms. It records variation in lexicographical works as well as in real speech and determines the degree of variability, based on recordings of native speakers. The thesis proceeds from the assumption that anglicisms were, are and will continue to be a highly relevant issue. In spite of all the measures aimed against their integration into the French lexis, they have their place in it, often in several pronunciation variants - whether due to the different inventory of phonemes or to sociolinguistic factors. KEY WORDS: anglicism, French, pronunciation, phonetic adaptation of loanwords
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Books on the topic "Pronunciation adaptation"

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Robust adaptation to non-native accents in automatic speech recognition. Berlin: Springer, 2002.

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Goronzy, Silke. Robust Adaptation to Non-Native Accents in Automatic Speech Recognition. Springer, 2003.

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Book chapters on the topic "Pronunciation adaptation"

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Qader, Raheel, Gwénolé Lecorvé, Damien Lolive, Marie Tahon, and Pascale Sébillot. "Statistical Pronunciation Adaptation for Spontaneous Speech Synthesis." In Text, Speech, and Dialogue, 92–101. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64206-2_11.

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Landini, Federico, Luciana Ferrer, and Horacio Franco. "Adaptation Approaches for Pronunciation Scoring with Sparse Training Data." In Speech and Computer, 87–97. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66429-3_8.

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Qader, Raheel, Gwénolé Lecorvé, Damien Lolive, and Pascale Sébillot. "Probabilistic Speaker Pronunciation Adaptation for Spontaneous Speech Synthesis Using Linguistic Features." In Statistical Language and Speech Processing, 229–41. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25789-1_22.

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Tahon, Marie, Raheel Qader, Gwénolé Lecorvé, and Damien Lolive. "Optimal Feature Set and Minimal Training Size for Pronunciation Adaptation in TTS." In Statistical Language and Speech Processing, 108–19. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45925-7_9.

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"Pronunciation Adaptation." In Robust Adaptation to Non-Native Accents in Automatic Speech Recognition, 79–104. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36290-8_8.

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Wu, Chung-Hsien, Hung-Yu Su, and Chao-Hong Liu. "Efficient Pronunciation Assessment of Taiwanese-Accented English Based on Unsupervised Model Adaptation and Dynamic Sentence Selection." In Multidisciplinary Computational Intelligence Techniques, 12–30. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1830-5.ch002.

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This chapter presents an efficient approach to personalized pronunciation assessment of Taiwanese-accented English. The main goal of this study is to detect frequently occurring mispronunciation patterns of Taiwanese-accented English instead of scoring English pronunciations directly. The proposed assessment help quickly discover personalized mispronunciations of a student, thus English teachers can spend more time on teaching or rectifying students’ pronunciations. In this approach, an unsupervised model adaptation method is performed on the universal acoustic models to recognize the speech of a specific speaker with mispronunciations and Taiwanese accent. A dynamic sentence selection algorithm, considering the mutual information of the related mispronunciations, is proposed to choose a sentence containing the most undetected mispronunciations in order to quickly extract personalized mispronunciations. The experimental results show that the proposed unsupervised adaptation approach obtains an accuracy improvement of about 2.1% on the recognition of Taiwanese-accented English speech.
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Kaun, Karen P. "Say It Down!" In Adaptation, Resistance and Access to Instructional Technologies, 87–107. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-61692-854-4.ch006.

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English Language Learners (ELLs) are in urgent need of instruction and tools to support their academic writing in English, which is essential to achievement in every academic subject and to overall school success (August & Shanahan, 2006). This chapter assesses the potential of speech recognition (SR) software, which through advances in research and development fueled by myriad commercial applications, now holds promise for new application in the classroom. SR has been studied for use in general education and by students with learning disabilities for academic writing. However, few studies to date have analyzed the impact of SR on ELLs’ writing. This study shows that SR software supported student pronunciation skills, which in turn may impact word fluency and text production. In addition, students used the software along with other semiotic tools (language, document-based questions, and other curricular material) to enhance their writing during the New York State Education Department Examination in Social Studies, which scores students’ essays for how they utilize higher level thinking skills and the extent to which they support their ideas with facts, examples, and details. Finally, this chapter explores how the constraints and affordances of the SR technology as a scaffold transformed with the students’ mastery of spoken language and literacy.
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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 "Pronunciation adaptation"

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Bodenstab, Nathan, and Mark Fanty. "Multi-Pass Pronunciation Adaptation." In 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.367207.

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Tahon, Marie, Raheel Qader, Gwénolé Lecorvé, and Damien Lolive. "Improving TTS with Corpus-Specific Pronunciation Adaptation." In Interspeech 2016. ISCA, 2016. http://dx.doi.org/10.21437/interspeech.2016-864.

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Kim, Yeon-Jun, Ann Syrdal, and Alistair Conkie. "Pronunciation lexicon adaptation for TTS voice building." In Interspeech 2004. ISCA: ISCA, 2004. http://dx.doi.org/10.21437/interspeech.2004-483.

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Yoo Rhee Oh, Mina Kim, and Hong Kook Kim. "Acoustic and pronunciation model adaptation for context-independent and context-dependent pronunciation variability of non-native speech." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4518601.

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Mertens, Timo, Kit Thambiratnam, and Frank Seide. "Subword-based multi-span pronunciation adaptation for recognizing accented speech." In Understanding (ASRU). IEEE, 2011. http://dx.doi.org/10.1109/asru.2011.6163940.

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Stemmer, Georg, Stefan Steidl, Christian Hacker, and Elmar Nöth. "Adaptation in the pronunciation space for non-native speech recognition." In Interspeech 2004. ISCA: ISCA, 2004. http://dx.doi.org/10.21437/interspeech.2004-11.

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Quesada Vázquez, Leticia. "Teaching English Pronunciation Online during the COVID-19 Crisis Outbreak." In Seventh International Conference on Higher Education Advances. Valencia: Universitat Politècnica de València, 2021. http://dx.doi.org/10.4995/head21.2021.12906.

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The abrupt emergence of the COVID-19 pandemic in spring 2020 forced tertiary professors to urgently adapt the face-to-face courses they were lecturing to emergency remote teaching. Researchers of different fields have started to investigate and share their thoughts on which are the best methodologies to guarantee a high-quality learning experience while coping with students’ anxiety and teachers’ lack of technical background. The present study examines the adaptation of an English pronunciation course at Rovira i Virgili university to the online setting imposed by the outbreak of the pandemic. The students who took the course were asked to fill in a satisfaction survey containing multiple choice, Likert scale and open questions on the different measures taken and the general progress of the course. Results show that students were higly satisfied with the adaptation of the course to the online context, and that the methods adopted and tools provided were useful and sufficient to continue with the adequate functioning of the course. Hence, this study is a sample of how to teach pronunciation remotely in particular, and how to successfully adapt a face-to-face university course to emergency remote teaching in general, guaranteeing students’ learning and engagement.
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Oh, Yoo Rhee, and Hong Kook Kim. "MLLR/MAP adaptation using pronunciation variation for non-native speech recognition." In Understanding (ASRU). IEEE, 2009. http://dx.doi.org/10.1109/asru.2009.5373299.

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Yang, Jian, Peishan Wu, and Dan Xu. "Mandarin Speech Recognition for Nonnative Speakers Based on Pronunciation Dictionary Adaptation." In 2008 6th International Symposium on Chinese Spoken Language Processing (ISCSLP). IEEE, 2008. http://dx.doi.org/10.1109/chinsl.2008.ecp.66.

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Lyu, Dau-Cheng, Eng-Siong Chng, and Haizhou Li. "Language diarization for conversational code-switch speech with pronunciation dictionary adaptation." In 2013 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP). IEEE, 2013. http://dx.doi.org/10.1109/chinasip.2013.6625316.

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