Dutch and German 3-year-olds’ representations of voicing alternations

The voicing contrast is neutralised syllable and word finally in Dutch and German, leading to alternations within the morphological paradigm (e.g. Dutch ‘bed(s)’, be[t] be[d]en, German ‘dog(s)’, Hun[t]-Hun[d]e). Despite structural similarity, language-specific morphological, phonological and lexical properties impact on the distribution of this alternation in the two languages. Previous acquisition research has focused on one language only, predominantly focusing on children’s production accuracy, concluding that alternations are not acquired until late in the acquisition process in either language. This paper adapts a perceptual method to investigate how voicing alternations are represented in the mental lexicon of Dutch and German 3-year-olds. Sensitivity to mispronunciations of voicing word-medially in plural forms was measured using a visual fixation procedure. Dutch children exhibited evidence of overgeneralising the voicing alternation, whereas German children consistently preferred the correct pronunciation to mispronunciations. Results indicate that the acquisition of voicing alternations is influenced by language-specific factors beyond the alternation itself

Methods for pronunciation assessment in computer aided language learning

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

Self-supervised end-to-end ASR for low resource L2 Swedish

Publisher Copyright: Copyright © 2021 ISCA.Unlike traditional (hybrid) Automatic Speech Recognition (ASR), end-to-end ASR systems simplify the training procedure by directly mapping acoustic features to sequences of graphemes or characters, thereby eliminating the need for specialized acoustic, language, or pronunciation models. However, one drawback of end-to-end ASR systems is that they require more training data than conventional ASR systems to achieve similar word error rate (WER). This makes it difficult to develop ASR systems for tasks where transcribed target data is limited such as developing ASR for Second Language (L2) speakers of Swedish. Nonetheless, recent advancements in selfsupervised acoustic learning, manifested in wav2vec models [1, 2, 3], leverage the available untranscribed speech data to provide compact acoustic representation that can achieve low WER when incorporated in end-to-end systems. To this end, we experiment with several monolingual and cross-lingual selfsupervised acoustic models to develop end-to-end ASR system for L2 Swedish. Even though our test is very small, it indicates that these systems are competitive in performance with traditional ASR pipeline. Our best model seems to reduce the WER by 7% relative to our traditional ASR baseline trained on the same target data.Peer reviewe

A Mobile App For Practicing Finnish Pronunciation Using Wav2vec 2.0

As Finland attracts more foreign talents, there are demands for self-learning tools to help second language (L2) speakers learn Finnish with proper feedback. However, there are few resources in L2 data in Finnish, especially focusing on the beginner level for adults. Moreover, since L2 adults are mainly busy studying or working in Finland, the application must allow users to practice anytime, anywhere. This thesis aims to address the above issues by developing a mobile app for beginner Finnish L2 learners to practice their pronunciation. The app would evaluate the users' speech samples, give feedback on their pronunciation, and then provide them with instructions in the form of text, photos, audio, and videos to help them improve their pronunciation. Due to the limited resources available, this work explores the wav2vec 2.0 model's capability for the application. We trained our models with the native Finnish speakers' corpus and used them to provide pronunciation feedback on L2 samples without any L2 training data. The results show that the models can detect mispronunciation on phoneme level about 60% of the time (Recall rate) compared to a native Finnish listener. By adding regularizations, selecting training datasets, and using a smaller model size, we achieved a comparable Recall rate of approximately 63% with a slightly lower Precision of around 29%. Compared to the state-of-the-art model in Finnish Automatic Speech Recognition, the trade-off resulted in a significantly faster response time

Learning [Voice]

The [voice] distinction between homorganic stops and fricatives is made by a number of acoustic correlates including voicing, segment duration, and preceding vowel duration. The present work looks at [voice] from a number of multidimensional perspectives. This dissertation\u27s focus is a corpus study of the phonetic realization of [voice] in two English-learning infants aged 1;1--3;5. While preceding vowel duration has been studied before in infants, the other correlates of post-vocalic voicing investigated here --- preceding F1, consonant duration, and closure voicing intensity --- had not been measured before in infant speech. The study makes empirical contributions regarding the development of the production of [voice] in infants, not just from a surface-level perspective but also with implications for the phonetics-phonology interface in the adult and developing linguistic systems. Additionally, several methodological contributions will be made in the use of large sized corpora and data modeling techniques. The study revealed that even in infants, F1 at the midpoint of a vowel preceding a voiced consonant was lower by roughly 50 Hz compared to a vowel before a voiceless consonant, which is in line with the effect found in adults. But while the effect has been considered most likely to be a physiological and nonlinguistic phenomenon in adults, it actually appeared to be correlated in the wrong direction with other aspects of [voice] here, casting doubt on a physiological explanation. Some of the consonant pairs had statistically significant differences in duration and closure voicing. Additionally, a preceding vowel duration difference was found and as well a preliminary indication of a developmental trend that suggests the preceding vowel duration difference is being learned. The phonetics of adult speech is also considered. Results are presented from a dialectal corpus study of North American English and a lab speech experiment which clarifies the relationship between preceding vowel duration and flapping and the relationship between [voice] and F1 in preceding vowels. Fluent adult speech is also described and machine learning algorithms are applied to learning the [voice] distinction using multidimensional acoustic input plus some lexical knowledge

Roles of domain-general auditory processing in spoken second-language vocabulary attainment in adulthood

Recently, scholars have begun to explore the hypothesis that individual differences in domain-general auditory perception, which has been identified as an anchor of L1 acquisition, could explain some variance in postpubertal L2 learners’ segmental and suprasegmental learning in immersive settings. The current study set out to examine the generalizability of the topic to the acquisition of higher-level linguistic production skills—that is the appropriate use of diverse, rich, and abstract vocabulary. The speech of 100 Polish-English bilinguals was elicited using an interview task, submitted to corpus-/rater-based linguistic analyses, and linked to their ability to discriminate sounds based on individual acoustic dimensions (pitch, duration, and amplitude). According to the results, those who attained more advanced L2 lexical proficiency demonstrated not only more relevant experience (extensive immersion and earlier age of arrival), but also more precise auditory perception ability