168 research outputs found

    Methods for pronunciation assessment in computer aided language learning

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 149-176).Learning a foreign language is a challenging endeavor that entails acquiring a wide range of new knowledge including words, grammar, gestures, sounds, etc. Mastering these skills all require extensive practice by the learner and opportunities may not always be available. Computer Aided Language Learning (CALL) systems provide non-threatening environments where foreign language skills can be practiced where ever and whenever a student desires. These systems often have several technologies to identify the different types of errors made by a student. This thesis focuses on the problem of identifying mispronunciations made by a foreign language student using a CALL system. We make several assumptions about the nature of the learning activity: it takes place using a dialogue system, it is a task- or game-oriented activity, the student should not be interrupted by the pronunciation feedback system, and that the goal of the feedback system is to identify severe mispronunciations with high reliability. Detecting mispronunciations requires a corpus of speech with human judgements of pronunciation quality. Typical approaches to collecting such a corpus use an expert phonetician to both phonetically transcribe and assign judgements of quality to each phone in a corpus. This is time consuming and expensive. It also places an extra burden on the transcriber. We describe a novel method for obtaining phone level judgements of pronunciation quality by utilizing non-expert, crowd-sourced, word level judgements of pronunciation. Foreign language learners typically exhibit high variation and pronunciation shapes distinct from native speakers that make analysis for mispronunciation difficult. We detail a simple, but effective method for transforming the vowel space of non-native speakers to make mispronunciation detection more robust and accurate. We show that this transformation not only enhances performance on a simple classification task, but also results in distributions that can be better exploited for mispronunciation detection. This transformation of the vowel is exploited to train a mispronunciation detector using a variety of features derived from acoustic model scores and vowel class distributions. We confirm that the transformation technique results in a more robust and accurate identification of mispronunciations than traditional acoustic models.by Mitchell A. Peabody.Ph.D

    Apraxia World: Deploying a Mobile Game and Automatic Speech Recognition for Independent Child Speech Therapy

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    Children with speech sound disorders typically improve pronunciation quality by undergoing speech therapy, which must be delivered frequently and with high intensity to be effective. As such, clinic sessions are supplemented with home practice, often under caregiver supervision. However, traditional home practice can grow boring for children due to monotony. Furthermore, practice frequency is limited by caregiver availability, making it difficult for some children to reach therapy dosage. To address these issues, this dissertation presents a novel speech therapy game to increase engagement, and explores automatic pronunciation evaluation techniques to afford children independent practice. Children with speech sound disorders typically improve pronunciation quality by undergoing speech therapy, which must be delivered frequently and with high intensity to be effective. As such, clinic sessions are supplemented with home practice, often under caregiver supervision. However, traditional home practice can grow boring for children due to monotony. Furthermore, practice frequency is limited by caregiver availability, making it difficult for some children to reach therapy dosage. To address these issues, this dissertation presents a novel speech therapy game to increase engagement, and explores automatic pronunciation evaluation techniques to afford children independent practice. The therapy game, called Apraxia World, delivers customizable, repetition-based speech therapy while children play through platformer-style levels using typical on-screen tablet controls; children complete in-game speech exercises to collect assets required to progress through the levels. Additionally, Apraxia World provides pronunciation feedback according to an automated pronunciation evaluation system running locally on the tablet. Apraxia World offers two advantages over current commercial and research speech therapy games; first, the game provides extended gameplay to support long therapy treatments; second, it affords some therapy practice independence via automatic pronunciation evaluation, allowing caregivers to lightly supervise instead of directly administer the practice. Pilot testing indicated that children enjoyed the game-based therapy much more than traditional practice and that the exercises did not interfere with gameplay. During a longitudinal study, children made clinically-significant pronunciation improvements while playing Apraxia World at home. Furthermore, children remained engaged in the game-based therapy over the two-month testing period and some even wanted to continue playing post-study. The second part of the dissertation explores word- and phoneme-level pronunciation verification for child speech therapy applications. Word-level pronunciation verification is accomplished using a child-specific template-matching framework, where an utterance is compared against correctly and incorrectly pronounced examples of the word. This framework identified mispronounced words better than both a standard automated baseline and co-located caregivers. Phoneme-level mispronunciation detection is investigated using a technique from the second-language learning literature: training phoneme-specific classifiers with phonetic posterior features. This method also outperformed the standard baseline, but more significantly, identified mispronunciations better than student clinicians

    Low-Resource Unsupervised NMT:Diagnosing the Problem and Providing a Linguistically Motivated Solution

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    Unsupervised Machine Translation hasbeen advancing our ability to translatewithout parallel data, but state-of-the-artmethods assume an abundance of mono-lingual data. This paper investigates thescenario where monolingual data is lim-ited as well, finding that current unsuper-vised methods suffer in performance un-der this stricter setting. We find that theperformance loss originates from the poorquality of the pretrained monolingual em-beddings, and we propose using linguis-tic information in the embedding train-ing scheme. To support this, we look attwo linguistic features that may help im-prove alignment quality: dependency in-formation and sub-word information. Us-ing dependency-based embeddings resultsin a complementary word representationwhich offers a boost in performance ofaround 1.5 BLEU points compared to stan-dardWORD2VECwhen monolingual datais limited to 1 million sentences per lan-guage. We also find that the inclusion ofsub-word information is crucial to improv-ing the quality of the embedding

    Design and evaluation of mobile computer-assisted pronunciation training tools for second language learning

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    The quality of speech technology (automatic speech recognition, ASR, and textto- speech, TTS) has considerably improved and, consequently, an increasing number of computer-assisted pronunciation (CAPT) tools has included it. However, pronunciation is one area of teaching that has not been developed enough since there is scarce empirical evidence assessing the effectiveness of tools and games that include speech technology in the field of pronunciation training and teaching. This PhD thesis addresses the design and validation of an innovative CAPT system for smart devices for training second language (L2) pronunciation. Particularly, it aims to improve learner’s L2 pronunciation at the segmental level with a specific set of methodological choices, such as learner’s first and second language connection (L1– L2), minimal pairs, a training cycle of exposure–perception–production, individualistic and social approaches, and the inclusion of ASR and TTS technology. The experimental research conducted applying these methodological choices with real users validates the efficiency of the CAPT prototypes developed for the four main experiments of this dissertation. Data is automatically gathered by the CAPT systems to give an immediate specific feedback to users and to analyze all results. The protocols, metrics, algorithms, and methods necessary to statistically analyze and discuss the results are also detailed. The two main L2 tested during the experimental procedure are American English and Spanish. The different CAPT prototypes designed and validated in this thesis, and the methodological choices that they implement, allow to accurately measuring the relative pronunciation improvement of the individuals who trained with them. Both rater’s subjective scores and CAPT’s objective scores show a strong correlation, being useful in the future to be able to assess a large amount of data and reducing human costs. Results also show an intensive practice supported by a significant number of activities carried out. In the case of the controlled experiments, students who worked with the CAPT tool achieved better pronunciation improvement values than their peers in the traditional in-classroom instruction group. In the case of the challenge-based CAPT learning game proposed, the most active players in the competition kept on playing until the end and achieved significant pronunciation improvement results.Departamento de Informática (Arquitectura y Tecnología de Computadores, Ciencias de la Computación e Inteligencia Artificial, Lenguajes y Sistemas Informáticos)Doctorado en Informátic

    FINE-GRAINED EMOTION DETECTION IN MICROBLOG TEXT

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    Automatic emotion detection in text is concerned with using natural language processing techniques to recognize emotions expressed in written discourse. Endowing computers with the ability to recognize emotions in a particular kind of text, microblogs, has important applications in sentiment analysis and affective computing. In order to build computational models that can recognize the emotions represented in tweets we need to identify a set of suitable emotion categories. Prior work has mainly focused on building computational models for only a small set of six basic emotions (happiness, sadness, fear, anger, disgust, and surprise). This thesis describes a taxonomy of 28 emotion categories, an expansion of these six basic emotions, developed inductively from data. This set of 28 emotion categories represents a set of fine-grained emotion categories that are representative of the range of emotions expressed in tweets, microblog posts on Twitter. The ability of humans to recognize these fine-grained emotion categories is characterized using inter-annotator reliability measures based on annotations provided by expert and novice annotators. A set of 15,553 human-annotated tweets form a gold standard corpus, EmoTweet-28. For each emotion category, we have extracted a set of linguistic cues (i.e., punctuation marks, emoticons, emojis, abbreviated forms, interjections, lemmas, hashtags and collocations) that can serve as salient indicators for that emotion category. We evaluated the performance of automatic classification techniques on the set of 28 emotion categories through a series of experiments using several classifier and feature combinations. Our results shows that it is feasible to extend machine learning classification to fine-grained emotion detection in tweets (i.e., as many as 28 emotion categories) with results that are comparable to state-of-the-art classifiers that detect six to eight basic emotions in text. Classifiers using features extracted from the linguistic cues associated with each category equal or better the performance of conventional corpus-based and lexicon-based features for fine-grained emotion classification. This thesis makes an important theoretical contribution in the development of a taxonomy of emotion in text. In addition, this research also makes several practical contributions, particularly in the creation of language resources (i.e., corpus and lexicon) and machine learning models for fine-grained emotion detection in text

    Towards A Practically Useful Text Simplification System

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    While there is a vast amount of text written about nearly any topic, this is often difficult for someone unfamiliar with a specific field to understand. Automated text simplification aims to reduce the complexity of a document, making it more comprehensible to a broader audience. Much of the research in this field has traditionally focused on simplification sub-tasks, such as lexical, syntactic, or sentence-level simplification. However, current systems struggle to consistently produce high-quality simplifications. Phrase-based models tend to make too many poor transformations; on the other hand, recent neural models, while producing grammatical output, often do not make all needed changes to the original text. In this thesis, I discuss novel approaches for improving lexical and sentence-level simplification systems. Regarding sentence simplification models, after noting that encouraging diversity at inference time leads to significant improvements, I take a closer look at the idea of diversity and perform an exhaustive comparison of diverse decoding techniques on other generation tasks. I also discuss the limitations in the framing of current simplification tasks, which prevent these models from yet being practically useful. Thus, I also propose a retrieval-based reformulation of the problem. Specifically, starting with a document, I identify concepts critical to understanding its content, and then retrieve documents relevant for each concept, re-ranking them based on the desired complexity level

    Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

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    On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges
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