Relevant bibliographies by topics / Mispronunciation

Academic literature on the topic 'Mispronunciation'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Mispronunciation"

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Bernier, Dana E., and Katherine S. White. "Toddlers Process Common and Infrequent Childhood Mispronunciations Differently for Child and Adult Speakers." Journal of Speech, Language, and Hearing Research 62, no. 11 (November 22, 2019): 4137–49. http://dx.doi.org/10.1044/2019_jslhr-h-18-0465.

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Purpose This study examined toddlers' processing of mispronunciations based on their frequency of occurrence in child speech and the speaker who produced them. Method One hundred twenty 22-month-olds were assigned to 1 of 4 conditions. Using the intermodal preferential looking paradigm, toddlers were shown visual displays containing 1 familiar object and 1 novel object, labeled by either a child or an adult. Familiar objects were labeled correctly or with a small mispronunciation that is either common in child speech (e.g., waisin for raisin) or infrequent (e.g., rauter for water). Results A significant interaction of speaker and type of mispronunciation showed that, for the child speaker, toddlers treated common and infrequent mispronunciations similarly, with equivalently sized mispronunciation penalties relative to correctly pronounced labels. In contrast, for the adult speaker, toddlers showed a large penalty for common mispronunciations, but infrequent mispronunciations were treated equivalently to correct pronunciations. Conclusion These results both reinforce and extend previous work on toddlers' processing of mispronunciations by revealing a complex interplay of speaker, type of mispronunciation, and specific contrast in toddlers' perceptions of mispronunciations.
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MANI, NIVEDITA, and KIM PLUNKETT. "Does size matter? Subsegmental cues to vowel mispronunciation detection." Journal of Child Language 38, no. 3 (November 1, 2010): 606–27. http://dx.doi.org/10.1017/s0305000910000243.

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ABSTRACTChildren look longer at a familiar object when presented with either correct pronunciations or small mispronunciations of consonants in the object's label, but not following larger mispronunciations. The current article examines whether children display a similar graded sensitivity to different degrees of mispronunciations of the vowels in familiar words, by testing children's sensitivity to 1-feature, 2-feature and 3-feature mispronunciations of the vowels of familiar labels: Children aged 1 ; 6 did not show a graded sensitivity to vowel mispronunciations, even when the trial length was increased to allow them more time to form a response. Two-year-olds displayed a robust sensitivity to increases in vowel mispronunciation size, differentiating between small and large mispronunciations. While this suggests that early lexical representations contain information about the features contributing to vocalic identity, we present evidence that this graded sensitivity is better explained by the acoustic characteristics of the different mispronunciation types presented to children.
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Donselaar, Wilma van. "Mispronunciation Detection." Language and Cognitive Processes 11, no. 6 (December 1996): 621–28. http://dx.doi.org/10.1080/016909696387024.

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Schmid, Peggy M., and Grace H. Yeni-Komshian. "The Effects of Speaker Accent and Target Predictability on Perception of Mispronunciations." Journal of Speech, Language, and Hearing Research 42, no. 1 (February 1999): 56–64. http://dx.doi.org/10.1044/jslhr.4201.56.

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This study makes use of a listening for mispronunciation task to examine how native English listeners perceive sentences produced by non-native speakers. The effects of target predictability and degree of foreign accent were investigated. Native and non-native speakers produced English sentences containing mispronunciation. Mispronunciations (MPs) were constructed by changing the initial phoneme of target words by a single distinctive feature along the dimensions of voicing, place, or manner. Results showed that listeners (a) were more accurate and faster in detecting MPs produced by native than non-native speakers, (b) were more accurate and faster in detecting MPs in predictable than unpredictable sentences, and (3) were more accurate in detecting MPs produced by non-native speakers with milder accents, as compared to heavier accents. These findings suggest that listening to fairly intelligible but accented speech requires increased processing effort—possibly because of subtle differences in intelligibility and increased variability characteristic of non-native speech.
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Zhang, Ying Shan Doris, and Kimberly Noels. "The Frequency and Importance of Accurate Heritage Name Pronunciation for Post-Secondary International Students in Canada." Journal of International Students 11, no. 3 (June 15, 2021): 608–27. http://dx.doi.org/10.32674/jis.v11i3.2232.

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International students’ names are often mispronounced, and this experience can have psychological and relational implications for some students’ cross-cultural adjustment. Little research, however, has examined why students are or are not bothered by mispronunciations. This study examined the impact of heritage name mispronunciation on 173 language-minority international students in Canada. The results indicated that although heritage name mispronunciations occurred frequently, only about half of the sample perceived correct pronunciation as important. Those who felt accurate pronunciation was important stressed that their name had a strong connection to their heritage and that mispronunciations were disrespectful of that significance. Those who felt accurate pronunciation was not important cited little personal connection to the name and accepted mispronunciations for reasons of efficiency. The findings suggest that accurate heritage name pronunciation can facilitate the adjustment of international students by fostering positive affect, communicative comfort, and relational closeness during cross-cultural interactions in the host countries.
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van der Feest, Suzanne V. H., and Paula Fikkert. "Building phonological lexical representations." Phonology 32, no. 2 (August 2015): 207–39. http://dx.doi.org/10.1017/s0952675715000135.

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This paper contributes to the ongoing debate on how much detail young children's word representations contain. We investigate early representations of place of articulation and voicing contrasts, inspired by previously attested asymmetrical patterns in children's early word productions. We tested Dutch-learning 20- and 24-month-olds’ perception of these fundamentally different contrasts in a mispronunciation-detection paradigm. Our results show that different kinds and directions of phonological changes yield different effects. Both 20- and 24-month-olds noticed coronal mispronunciations of labials, but not vice versa. The 24-month-olds noticed voiced mispronunciations of voiceless stops, but not vice versa, while the 20-month-olds failed to notice any voicing mispronunciations. We argue that early lexical representations are specified in very systematic ways, that not all phonological contrasts are encoded at the same time and that the phonological system of a language determines which contrasts are specified first in the representations of early words.
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RAMON-CASAS, MARTA, CHRISTOPHER T. FENNELL, and LAURA BOSCH. "Minimal-pair word learning by bilingual toddlers: the Catalan /e/-/ɛ/ contrast revisited." Bilingualism: Language and Cognition 20, no. 3 (November 18, 2016): 649–56. http://dx.doi.org/10.1017/s1366728916001115.

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Twelve-month-old bilingual and monolingual infants show comparable phonetic discrimination skills for vowels belonging to their native language/s. However, Catalan–Spanish bilingual toddlers, but not Catalan monolinguals, appear insensitive to a vowel mispronunciation in familiar words involving the Catalan–Specific /e/-/ɛ/ contrast. Here bilingual and monolingual toddlers were tested in a challenging minimal-pair word learning task involving that contrast (i.e., [bepi]-[bɛpi]). Both groups succeeded, suggesting that bilinguals can successfully use their phonetic categories to phonologically encode novel words. It is argued that bilinguals’ impoverished vowel representations in familiar words might be the result of experiential input factors (e.g., cognate words and mispronunciations due to accented speech).
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Shufang, Zhang. "Design of an Automatic English Pronunciation Error Correction System Based on Radio Magnetic Pronunciation Recording Devices." Journal of Sensors 2021 (December 27, 2021): 1–12. http://dx.doi.org/10.1155/2021/5946228.

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In this paper, a system for automatic detection and correction of mispronunciation of native Chinese learners of English by speech recognition technology is designed with the help of radiomagnetic pronunciation recording devices and computer-aided software. This paper extends the standard pronunciation dictionary by predicting the phoneme confusion rules in the language learner’s pronunciation that may lead to mispronunciation and generates an extended pronunciation dictionary containing the standard pronunciation of each word and the possible mispronunciation variations, and automatic speech recognition uses the extended pronunciation dictionary to detect and diagnose the learner’s mispronunciation of phonemes and provides real-time feedback. It is generated by systematic crosslinguistic phonological comparative analysis of the differences in phoneme pronunciation with each other, and a data-driven approach is used to do automatic phoneme recognition of learner speech and analyze the mapping relationship between the resulting mispronunciation and the corresponding standard pronunciation to automatically generate additional phoneme confusion rules. In this paper, we investigate various aspects of several issues related to the automatic correction of English pronunciation errors based on radiomagnetic pronunciation recording devices; design the general block diagram of the system, etc.; and discuss some key techniques and issues, including endpoint detection, feature extraction, and the system’s study of pronunciation standard algorithms, analyzing their respective characteristics. Finally, we design and implement a model of an automatic English pronunciation error correction system based on a radiomagnetic pronunciation recording device. Based on the characteristics of English pronunciation, the correction algorithm implemented in this system uses the similarity and pronunciation duration ratings based on the log posterior probability, which combines the scores of both, and standardizes this system scoring through linear mapping. This system can achieve the purpose of automatic recognition of English mispronunciation correction and, at the same time, improve the user’s spoken English pronunciation to a certain extent.
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Buditama, JK Aditya Christya, Catur Atmaji, and Agfianto Eko Putra. "Deteksi Kesalahan Pengucapan Huruf Jawa Carakan dengan Jaringan Syaraf Tiruan Perambatan Balik." IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) 11, no. 2 (October 31, 2021): 155. http://dx.doi.org/10.22146/ijeis.53437.

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Javanese is an Indonesian culture which needs to be preserved, but many Javanese students make mistakes in the pronunciation of Javanese letters and find it difficult to analyze errors by human teachers because of the limited time and subjective assessment, so a system is needed to detect incorrect pronunciation of Javanese letters. Mispronunciation detection system has been widely applied in foreign languages, but the system has not been implemented for Javanese carakan letters. This research develops the Javanese letters mispronunciation detection system using Back-Propagation Artificial Neural Networks (BP-ANN). The dataset is obtained from the recorded pronunciation of hanacaraka texts by 24 speakers with 5 repetitions. ALNS method then used to automatically segment the signal into syllables. ANN-PB use statistical value of Mel-Frequency Cepstral Coefficient (MFCC) method with 7 and 14 coefficients. 10-Fold Cross Validation is used to validate and test the system. The Javanese mispronunciation detection using 7MFCC coefficients produces the highest accuracy of 80,07%. While the Javanese mispronunciation detection using 14 MFCC coefficients produces an accuracy of 82.36% at the highest.
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Ambalegin, Ambalegin, and Fasaaro Hulu. "EFL LEARNERS’ PHONOLOGICAL INTERFERENCE OF ENGLISH ARTICULATION." JURNAL BASIS 6, no. 2 (October 26, 2019): 145. http://dx.doi.org/10.33884/basisupb.v6i2.1415.

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This research investigated the mispronunciation of Putera Batam University EFL learners by adapting the standard of Received Pronunciation (RP) and the factors of English vowels and consonants mispronunciation. This descriptive qualitative research applied observational method with participatory technique in collecting data, and articulatory identity method in analyzing the data. The English mispronunciation was found in the EFL learners’ English pronunciation. The consonant sounds /ð/, /θ/, /th/, /z/, /r/, /ʃ/, /ʧ/, /ʤ/, /nj/, and consonant-closed syllable sound of /k/ were pronounced incorrectly. The consonant sound /ð/ was pronounced as /d/, /θ/ as /t/, /th/ as /t/, /z/as /ɉ/, /r/ as /ɾ/, /ʃ/ as /s/, /ʧ/ as /s/, /ʤ/ as /d/, and /nj/ is pronounced as /ɲ/. Consonant-closed syllable sound of /k/ is articulated as /Ɂ/. The vowel sounds /ə/ and /æ/ were pronounced incorrectly as /e/ and the diphthong sound /eɪ/ were pronounced incorrectly as /e/. These mispronunciation phenomena were caused by some factors based on their background. The factors were; the mother tongue interference (native language), the differences between Indonesian and English sound systems (phonetic ability), the educational background, and the environmental background (amount of exposure).
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Dissertations / Theses on the topic "Mispronunciation"

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Koniaris, Christos. "Perceptually motivated speech recognition and mispronunciation detection." Doctoral thesis, KTH, Tal-kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-102321.

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This doctoral thesis is the result of a research effort performed in two fields of speech technology, i.e., speech recognition and mispronunciation detection. Although the two areas are clearly distinguishable, the proposed approaches share a common hypothesis based on psychoacoustic processing of speech signals. The conjecture implies that the human auditory periphery provides a relatively good separation of different sound classes. Hence, it is possible to use recent findings from psychoacoustic perception together with mathematical and computational tools to model the auditory sensitivities to small speech signal changes. The performance of an automatic speech recognition system strongly depends on the representation used for the front-end. If the extracted features do not include all relevant information, the performance of the classification stage is inherently suboptimal. The work described in Papers A, B and C is motivated by the fact that humans perform better at speech recognition than machines, particularly for noisy environments. The goal is to make use of knowledge of human perception in the selection and optimization of speech features for speech recognition. These papers show that maximizing the similarity of the Euclidean geometry of the features to the geometry of the perceptual domain is a powerful tool to select or optimize features. Experiments with a practical speech recognizer confirm the validity of the principle. It is also shown an approach to improve mel frequency cepstrum coefficients (MFCCs) through offline optimization. The method has three advantages: i) it is computationally inexpensive, ii) it does not use the auditory model directly, thus avoiding its computational cost, and iii) importantly, it provides better recognition performance than traditional MFCCs for both clean and noisy conditions. The second task concerns automatic pronunciation error detection. The research, described in Papers D, E and F, is motivated by the observation that almost all native speakers perceive, relatively easily, the acoustic characteristics of their own language when it is produced by speakers of the language. Small variations within a phoneme category, sometimes different for various phonemes, do not change significantly the perception of the language’s own sounds. Several methods are introduced based on similarity measures of the Euclidean space spanned by the acoustic representations of the speech signal and the Euclidean space spanned by an auditory model output, to identify the problematic phonemes for a given speaker. The methods are tested for groups of speakers from different languages and evaluated according to a theoretical linguistic study showing that they can capture many of the problematic phonemes that speakers from each language mispronounce. Finally, a listening test on the same dataset verifies the validity of these methods.

QC 20120914


European Union FP6-034362 research project ACORNS
Computer-Animated language Teachers (CALATea)
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Lee, Ann Ph D. Massachusetts Institute of Technology. "A comparison-based approach to mispronunciation detection." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/75660.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 89-92).
This thesis focuses on the problem of detecting word-level mispronunciations in nonnative speech. Conventional automatic speech recognition-based mispronunciation detection systems have the disadvantage of requiring a large amount of language-specific, annotated training data. Some systems even require a speech recognizer in the target language and another one in the students' native language. To reduce human labeling effort and for generalization across all languages, we propose a comparison-based framework which only requires word-level timing information from the native training data. With the assumption that the student is trying to enunciate the given script, dynamic time warping (DTW) is carried out between a student's utterance (nonnative speech) and a teacher's utterance (native speech), and we focus on detecting mis-alignment in the warping path and the distance matrix. The first stage of the system locates word boundaries in the nonnative utterance. To handle the problem that nonnative speech often contains intra-word pauses, we run DTW with a silence model which can align the two utterances, detect and remove silences at the same time. In order to segment each word into smaller, acoustically similar, units for a finer-grained analysis, we develop a phoneme-like unit segmentor which works by segmenting the selfsimilarity matrix into low-distance regions along the diagonal. Both phone-level and wordlevel features that describe the degree of mis-alignment between the two utterances are extracted, and the problem is formulated as a classification task. SVM classifiers are trained, and three voting schemes are considered for the cases where there are more than one matching reference utterance. The system is evaluated on the Chinese University Chinese Learners of English (CUCHLOE) corpus, and the TIMIT corpus is used as the native corpus. Experimental results have shown 1) the effectiveness of the silence model in guiding DTW to capture the word boundaries in nonnative speech more accurately, 2) the complimentary performance of the word-level and the phone-level features, and 3) the stable performance of the system with or without phonetic units labeling.
by Ann Lee.
S.M.
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Ge, Zhenhao. "Mispronunciation detection for language learning and speech recognition adaptation." Thesis, Purdue University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3613127.

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The areas of "mispronunciation detection" (or "accent detection" more specifically) within the speech recognition community are receiving increased attention now. Two application areas, namely language learning and speech recognition adaptation, are largely driving this research interest and are the focal points of this work.

There are a number of Computer Aided Language Learning (CALL) systems with Computer Aided Pronunciation Training (CAPT) techniques that have been developed. In this thesis, a new HMM-based text-dependent mispronunciation system is introduced using text Adaptive Frequency Cepstral Coefficients (AFCCs). It is shown that this system outperforms the conventional HMM method based on Mel Frequency Cepstral Coefficients (MFCCs). In addition, a mispronunciation detection and classification algorithm based on Principle Component Analysis (PCA) is introduced to help language learners identify and correct their pronunciation errors at the word and syllable levels.

To improve speech recognition by adaptation, two projects have been explored. The first one improves name recognition by learning acceptable variations in name pronunciations, as one of the approaches to make grammar-based name recognition adaptive. The second project is accent detection by examining the shifting of fundamental vowels in accented speech. This approach uses both acoustic and phonetic information to detect accents and is shown to be beneficial with accented English. These applications can be integrated into an automated international calling system, to improve recognition of callers' names and speech. It determines the callers' accent based in a short period of speech. Once the type of accents is detected, it switches from the standard speech recognition engine to an accent-adaptive one for better recognition results.

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Chute, Erin Marie. "The Nature of Phonological Representations in Adults and Children: Evidence of Mispronunciation Detection." Thesis, The University of Arizona, 2011. http://hdl.handle.net/10150/144243.

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Ainsworth, Stephanie. "Development of phonological representations in young children." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/development-of-phonological-representations-in-young-children(91cb6b48-d2fe-46bd-acf4-f0792df69501).html.

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The development of phonological representations remains a hot topic within both the developmental and neural network literature. Historically, theoretical accounts have fallen within one of two camps: the accessibility account which proposes that phonological representations are adult-like from infancy (Rozin & Gleitman, 1977; Liberman, Shankweiler & Liberman, 1989) and the emergent account which proposes that phonological representations become gradually restructured over development (Metsala & Walley, 1998; Ventura, Kolinsky, Fernandes, Querido & Morais, 2007; Ziegler & Goswami, 2005). Within this thesis we tested predictions made by the accessibility account and key variants of the emergent account using data from both behavioural (Chapters 2, 3 and 4) and neural network studies (Chapter 5). The novel measures used within Chapters 2 to 4 were devised to allow us to contrast implicit measures of phonological representation (PR) which probe the segmentedness of the representations themselves, with explicit PR measures which tap into children’s conscious awareness of phonological segments. Within Chapter 2 we present evidence that while explicit awareness of phonological structure is dependent on letter-sound knowledge, implicit sensitivity to the segments within words emerges independent of literacy. Within Chapter 3 a longitudinal study investigated the segmentedness of children’s phonological representations at the rime and phoneme level. These results demonstrate that implicit sensitivity to both rime and phoneme segments is driven by vocabulary growth and is not dependent on letter-sound knowledge. The results within Chapter 3 also suggest that, while awareness of rime segments emerges naturally through oral language experience, explicit awareness of individual phonemes is related to letter-sound knowledge. In Chapter 4 we explored the idea of global versus phonemic representation using a mispronunciation reconstruction task. We found that sensitivity to both global and phonemic similarity increased over time, but with global sensitivity reaching adult levels early on in development. In Chapter 5 a neural network was trained on the mappings between real acoustic input and articulatory output data allowing us to simulate the development of phonological representations computationally. The simulation data provide further evidence of a developmental increase in sensitivity to both global and phonemic similarity within a preliterate model. Taken together, the results provide strong evidence that as children’s vocabularies grow they become increasingly sensitive to both the global properties and segmental structure of words, independent of literacy experience. Children’s explicit awareness of phonemes, on the other hand, seems to emerge as a consequence of learning the correspondence between letters and sounds. Within the context of the wider literature, the current results are most consistent with the PRIMIR framework which predicts early detailed phonetic representations alongside gradually emerging phonemic categories (Werker & Curtin, 2005). This thesis underlines the importance of using implicit measures when trying to probe the representations themselves rather than children’s conscious awareness of them. The thesis also represents an important step towards modelling the emergence of segmental representation computationally using real speech data.
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Gong, Rong. "Automatic assessment of singing voice pronunciation: a case study with Jingju music." Doctoral thesis, Universitat Pompeu Fabra, 2018. http://hdl.handle.net/10803/664421.

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Online learning has altered music education remarkable in the last decade. Large and increasing amount of music performing learners participate in online music learning courses due to the easy-accessibility and boundless of time-space constraints. Singing can be considered the most basic form of music performing. Automatic singing voice assessment, as an important task in Music Information Retrieval (MIR), aims to extract musically meaningful information and measure the quality of learners' singing voice. Singing correctness and quality is culture-specific and its assessment requires culture-aware methodologies. Jingju (also known as Beijing opera) music is one of the representative music traditions in China and has spread to many places in the world where there are Chinese communities. Our goal is to tackle unexplored automatic singing voice pronunciation assessment problems in jingju music, to make the current eurogeneric assessment approaches more culture-aware, and in return, to develop new assessment approaches which can be generalized to other musical traditions.
El aprendizaje en línea ha cambiado notablemente la educación musical en la pasada década. Una cada vez mayor cantidad de estudiantes de interpretación musical participan en cursos de aprendizaje musical en línea por su fácil accesibilidad y no estar limitada por restricciones de tiempo y espacio. Puede considerarse el canto como la forma más básica de interpretación. La evaluación automática de la voz cantada, como tarea importante en la disciplina de Recuperación de Información Musical (MIR por sus siglas en inglés) tiene como objetivo la extracción de información musicalmente significativa y la medición de la calidad de la voz cantada del estudiante. La corrección y calidad del canto son específicas a cada cultura y su evaluación requiere metodologías con especificidad cultural. La música del jingju (también conocido como ópera de Beijing) es una de las tradiciones musicales más representativas de China y se ha difundido a muchos lugares del mundo donde existen comunidades chinas.Nuestro objetivo es abordar problemas aún no explorados sobre la evaluación automática de la voz cantada en la música del jingju, hacer que las propuestas eurogenéticas actuales sobre evaluación sean más específicas culturalmente, y al mismo tiempo, desarrollar nuevas propuestas sobre evaluación que puedan ser generalizables para otras tradiciones musicales.
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Hsu, Yao-Chi, and 許曜麒. "Mispronunciation Detection with Evaluation Metric-related Training Criteria." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/13227980116786772162.

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碩士
國立臺灣師範大學
資訊工程學系
104
Mispronunciation detection and diagnosis are part and parcel of a computer assisted pronunciation training (CAPT) system, collectively facilitating second-language (L2) learners to pinpoint erroneous pronunciations in a given utterance so as to improve their spoken proficiency. This thesis presents a continuation of such a general line of research and the major contributions are three-fold. First, we propose an effective training approach that estimates the deep neural network based acoustic models involved in the mispronunciation detection process by optimizing an objective directly linked to the ultimate evaluation metric. Second, we investigate the extent to which, the subsequent mispronunciation diagnosis can benefit from using these specifically trained acoustic models. Third, we recast mispronunciation diagnosis as a classification problem and leverage a rich set of features for the idea to work. A series of experiments on a Mandarin mispronunciation detection and diagnosis task seem to show the performance merits of the proposed methods.
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Chao, Yung-Chun, and 趙詠純. "Research on Taiwan Japanese Learners’ Mispronunciation of Two-Character Idioms." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/60452963336252767478.

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碩士
國立臺灣大學
日本語文學研究所
101
Research on Taiwan Japanese Learners’ Mispronunciation of Two-Character Idioms ABSTRACT Influenced by the times of introduction of Chinese character’s pronunciation, some Chinese characters in Japanese have pronunciations similar to Chinese, and some have totally different pronunciations from Chinese. Consequently, for Taiwanese Japanese learners whose mother language is Chinese, on judging Japanese pronunciations, if they analogize the pronunciations based on Chinese character’s pronunciation, it may help sometimes, but sometimes it will lead to mispronunciations as well. On the other hand, every syllable of Japanese word has the same pronunciation length. As a result, when the pronunciation length changes, the meaning may change with it, which may raise difficulty in learning Japanese. This research expects to have positive influence on learning Chinese characters in Japanese. It adopted Taiwanese with Chinese as their mother language as the research subjects. From the perspective of mispronunciation, it explored their learning situation of two-character idioms, and proposed the problematic points and the methods for improvement. Physically, it categorized the experiment into three items; that is, “Pronunciation Similar to Chinese”, “Similar Pronunciation in both Chinese and Japanese”, and “Sense of Syllable in Japanese”. And, the experiment placed the emphasis on two-character Chinese vocabulary. By means of questionnaires, it observed mispronunciation tendency and the causes of learners in different learning levels with an attempt to conduct examination. The research results are as follows: 1. In “Pronunciation Similar to Chinese”, we have observed that as the learning level raises, the condition of mispronunciation decreases. Besides, it has been found that the causes of mispronunciation are influenced by transformation of Chinese knowledge to Japanese knowledge. Students who mispronounce by exerting Chinese knowledge are mainly learners in the beginning level. Though Chinese pronunciation continues to have an impact on their Japanese pronunciation, students in middle level gradually exert Japanese knowledge they have absorbed. As for students in high level, mispronunciation still occurs due to influence of Chinese knowledge, but students tend to exert Japanese knowledge to learn the pronunciation primarily. 2. In “Similar Pronunciation in both Chinese and Japanese”, in regard of two-character idioms, we saw the mispronunciation rates of the first character and the second character are similar. In addition, in tendency of “mispronunciation of nasal sound”, we found that it is easier for the students to mispronounce the first character in two-character idioms. As for learners’ way to exert knowledge, it is similar to that of those in “Pronunciation Similar to Chinese”. 3. In “Sense of Syllable in Japanese”, we found that no matter it is two-character idiom with long pronunciation or that with short pronunciation, the mispronunciation of the second character is higher in both cases. However, in Chinese, “decrease of syllables” in Chinese vocabulary with long pronunciation occurs in the first character more often, while “increase of syllables” in Chinese vocabulary with short pronunciation occurs in the second character more often. Additionally, for Japanese learners in middle level, we found that they are influenced by Japanese knowledge gradually.
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Lin, Yi-Ju, and 林奕儒. "Mispronunciation Detection and Diagnosis Combining Prosodic Features and Phonetic Features." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/2n6r3r.

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碩士
國立臺灣師範大學
資訊工程學系
107
The main idea of this thesis is to discuss the assists of the multi-task deep neural network model and prosody characteristics in mispronunciation detection and diagnosis (MDD). The purpose of computer assisted pronunciation training (CAPT) is to help second-language (L2) learners automatically correcting the mistaken pronunciation. Computer assisted pronunciation training can be divided into mispronunciation detection and mispronunciation diagnosis. This paper mainly focuses on three aspects. First, we explore the benefits using the combined features of prosodic and phonetic characteristic in mispronunciation detection and diagnosis task. Second, we use multi-task learning models to help solving the data unbalanced problem. Last but not least, we combine likelihood-based scoring (GOP) method and classification-based scoring method in order to achieve better detection and diagnosis results. The result of experiments shows that phonetic features work better when we need to detect the mispronunciation. On the contrary, prosodic features are more helpful to mispronunciation diagnosis task.
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Chen, Xuan-Bo, and 陳宣伯. "Mandarin Mispronunciation Detection and Diagnosis Feedback Using Articulatory Attributes Based Multi-task Learning." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/2a8u7x.

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碩士
國立臺灣大學
資訊工程學研究所
107
This paper presents our research on computer assisted pronunciation training (CAPT). We focus on mispronunciation detection and articulation feedback. We propose taking into account the speech attributes, namely place and manner of articulation, in the assessment models to improve mispronunciation detection and return precise articulation feedback to learners. We train a discriminative articulatory model based on time-delay neural networks (TDNNs) with the multi-task learning strategy to give the articulatory score and a TDNN-based acoustic model to give the phonetic score. In testing, the system detects mispronunciations and returns precise articulation feedback based on both the phonetic and articulatory scores. The results of experiments conducted on the MATBN Mandarin Chinese broadcast news corpus show that the proposed models outperform the Gaussian mixture model (GMM)-based and deep neural network (DNN)-based baselines in terms of equal error rate (EER) and diagnostic accuracy (DA). Furthermore, our mispronunciation detection system should work in any language, although the current system focuses on Mandarin.
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Books on the topic "Mispronunciation"

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1944-, Steele Phil, ed. The word for the day: 65 years of Bob Steele's wit and wisdom on mispronunciation. Newington, Conn: Connecticut River Press, 2002.

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Norman, Philip. Your walrus hurt the one you love: Malapropisms, mispronunciations and linguistic cock-ups. London: Elm Tree, 1985.

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Your walrus hurt the one you love: Malapropisms, mispronunciations, and linguistic cock-ups. London: Elm Tree Books, 1985.

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The big book of beastly mispronunciations: The complete opinionated guide for the careful speaker. Boston: Houghton Mifflin, 1999.

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The big book of beastly mispronunciations: The complete opinionated guide for the careful speaker. 2nd ed. Boston: Houghton Mifflin, 2005.

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Elster, Charles Harrington. Is there a cow in Moscow?: More beastly mispronunciations and sound advice : another opinionated guide for the well-spoken. New York: Collier Books, 1990.

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Kotzor, Sandra, Allison Wetterlin, and Aditi Lahiri. Bengali geminates. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198754930.003.0009.

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Bengali has a robust medial geminate/singleton contrast across oral stops and nasals in five places of articulation. This chapter presents a synchronic account of the phonological system involving the consonantal length contrast, which supports an asymmetric moraic representation of geminates. Based on these representational assumptions, two EEG and two behavioural experiments were conducted to investigate the processing of this geminate/singleton contrast by Bengali native speakers. The results reveal a processing asymmetry for the duration contrast: the processing of the duration contrast is indeed asymmetric: a geminate mispronunciation is accepted for a singleton real word, while the reverse is not the case. This provides evidence that the lexical representation of the duration contrast must be asymmetric and thus privative rather than equipollent.
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Neuffer, Claude, and Irene Neuffer. Correct Mispronunciations of South Carolina Names. University of South Carolina Press, 2020.

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Neuffer, Claude, and Irene Neuffer. Correct Mispronunciations of South Carolina Names. University of South Carolina Press, 2020.

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Neuffer, Claude, and Irene Neuffer. Correct Mispronunciations of Some South Carolina Names. Univ of South Carolina Pr, 1989.

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Book chapters on the topic "Mispronunciation"

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Minh, Nguyen Quang, and Phan Duy Hung. "The System for Detecting Vietnamese Mispronunciation." In Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications, 452–59. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-8062-5_32.

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Zhang, Li, Chao Huang, Min Chu, Frank Soong, Xianda Zhang, and Yudong Chen. "Automatic Detection of Tone Mispronunciation in Mandarin." In Chinese Spoken Language Processing, 590–601. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11939993_61.

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Karbasi, Mahdie, Steffen Zeiler, Jan Freiwald, and Dorothea Kolossa. "Toward Robust Mispronunciation Detection via Audio-Visual Speech Recognition." In Advances in Computational Intelligence, 655–66. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20518-8_54.

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Huang, Guimin, Changxiu Qin, Yan Shen, and Ya Zhou. "Improvement in Text-Dependent Mispronunciation Detection for English Learners." In Advances in Intelligent Systems and Computing, 131–38. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38771-0_13.

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Pu, Shi, Lee Becker, and Misaki Kato. "Automatic Identification of Non-native English Speaker’s Phoneme Mispronunciation Tendencies." In Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium, 608–11. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11647-6_126.

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Chen, Berlin, and Yao-Chi Hsu. "Mandarin Chinese Mispronunciation Detection and Diagnosis Leveraging Deep Neural Network Based Acoustic Modeling and Training Techniques." In Chinese Language Learning Sciences, 217–34. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3570-9_11.

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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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Martinez, Marcos E., Francisco López-Orozco, Karla Olmos-Sánchez, and Julia Patricia Sánchez-Solís. "Mispronunciation Detection and Diagnosis Through a Chatbot." In Handbook of Research on Natural Language Processing and Smart Service Systems, 31–45. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4730-4.ch002.

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The interaction between humans and machines has evolved; thus, the idea of being able to communicate with computers as we usually do with other people is becoming increasingly closer to coming true. Nowadays, it is common to come across intelligent systems named chatbots, which allow people to communicate by using natural language to hold conversations related to a specific domain. Chatbots have gained popularity in different kinds of sectors, such as customer service, marketing, sales, e-commerce, e-learning, travel, and even in education itself. This chapter aims to present a chatbot-based approach to learning English as a second language by using computer-assisted language learning systems.
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Keyes, Ralph. "Coined by Chance." In The Hidden History of Coined Words, 16–28. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780190466763.003.0002.

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Among the many of ways in which words are born, one seldom receives its due: happenstance. Sources of new words can be fluky. Many new words have resulted from misprints (derring do), befuddlement (decider), and mispronunciation (quark). Proust noted how many terms that French speakers took pride in pronouncing correctly resulted from “blunders made by Gaulish mouths, mispronouncing Latin and Saxon words.” Literary scholar Walter Redfern called such coinage-by-mishap blunderful. Linguists are keenly aware of the role mishaps can play in word creation. In Aspects of Language, Dwight Bolinger and Donald Sears discussed how often simple mistakes fertilize our lexicon. As in the natural world, such mistakes – typos, misspelling, mistranslation – have been a key source of evolutionary change.
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Davidson, Michael. "Misspeaking Poetics." In Distressing Language, 72–97. NYU Press, 2022. http://dx.doi.org/10.18574/nyu/9781479813827.003.0004.

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In chapter 3, I look at artists who have used their speech impediments to generate new work—Norma Cole’s “Speech Production” and Jordan Scott’s Blert. One section deals with various artists’ interpretation of the shibboleth as it applies to more recent history. Paul Celan’s poem “Shibboleth” responds to the horrors of the Holocaust; Doris Salcedo’s Shibboleth carves a crack in the Tate Modern’s massive gallery to memorialize global subjects who fall through cracks in national narratives; Caroline Bergvall’s “Say, ‘Parsley’” remembers the murder of Haitians by Dominican military for their inability to pronounce the Spanish word for parsley, perejil. Jordan Scott’s Blert offers an exercise in transforming his “blurts,” caused by a lifelong stammer, into sound poems based on ecopolitical issues that link his speech impediment to the global environment. The work of Jordanian-Lebanese artist Lawrence Abu Hamdan involves studying acoustic technologies and surveillance in determining how mispronunciation is used to identify “true” from “false” asylum seekers.
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Conference papers on the topic "Mispronunciation"

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Feng Zhang, Chao Huang, Frank K. Soong, Min Chu, and Renhua Wang. "Automatic mispronunciation detection for Mandarin." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4518800.

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Chen, Yuqiang, Chao Huang, and Frank Soong. "Improving mispronunciation detection using machine learning." In ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009. http://dx.doi.org/10.1109/icassp.2009.4960721.

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Yuan, Hua, Junhong Zhao, and Jia Liu. "Improve mispronunciation detection with Tandem feature." In 2012 8th International Symposium on Chinese Spoken Language Processing (ISCSLP 2012). IEEE, 2012. http://dx.doi.org/10.1109/iscslp.2012.6423538.

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Xu, Xiaoshuo, Yueteng Kang, Songjun Cao, Binghuai Lin, and Long Ma. "Explore wav2vec 2.0 for Mispronunciation Detection." In Interspeech 2021. ISCA: ISCA, 2021. http://dx.doi.org/10.21437/interspeech.2021-777.

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Lee, Ann, and James Glass. "Mispronunciation detection without nonnative training data." In Interspeech 2015. ISCA: ISCA, 2015. http://dx.doi.org/10.21437/interspeech.2015-232.

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Liu, Changl, Fuping Pan, Fengpei Ge, Bin Dong, and Yonghong Yan. "Forward optimal measures for automatic mispronunciation detection." In 2010 7th International Symposium on Chinese Spoken Language Processing (ISCSLP). IEEE, 2010. http://dx.doi.org/10.1109/iscslp.2010.5684844.

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Lee, Ann, and James Glass. "A comparison-based approach to mispronunciation detection." In 2012 IEEE Spoken Language Technology Workshop (SLT 2012). IEEE, 2012. http://dx.doi.org/10.1109/slt.2012.6424254.

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Ronen, Orith, Leonardo Neumeyer, and Horacio Franco. "Automatic detection of mispronunciation for language instruction." In 5th European Conference on Speech Communication and Technology (Eurospeech 1997). ISCA: ISCA, 1997. http://dx.doi.org/10.21437/eurospeech.1997-231.

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Dong, Wenwei, and Yanlu Xie. "Normalization of GOP for Chinese Mispronunciation Detection." In 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2019. http://dx.doi.org/10.1109/apsipaasc47483.2019.9023085.

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Ge, Zhenhao, Sudhendu R. Sharma, and Mark J. T. Smith. "Adaptive frequency cepstral coefficients for word mispronunciation detection." In 2011 4th International Congress on Image and Signal Processing (CISP). IEEE, 2011. http://dx.doi.org/10.1109/cisp.2011.6100685.

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