A computational model for studying L1’s effect on L2 speech learning

abstract: Much evidence has shown that first language (L1) plays an important role in the formation of L2 phonological system during second language (L2) learning process. This combines with the fact that different L1s have distinct phonological patterns to indicate the diverse L2 speech learning outcomes for speakers from different L1 backgrounds. This dissertation hypothesizes that phonological distances between accented speech and speakers' L1 speech are also correlated with perceived accentedness, and the correlations are negative for some phonological properties. Moreover, contrastive phonological distinctions between L1s and L2 will manifest themselves in the accented speech produced by speaker from these L1s. To test the hypotheses, this study comes up with a computational model to analyze the accented speech properties in both segmental (short-term speech measurements on short-segment or phoneme level) and suprasegmental (long-term speech measurements on word, long-segment, or sentence level) feature space. The benefit of using a computational model is that it enables quantitative analysis of L1's effect on accent in terms of different phonological properties. The core parts of this computational model are feature extraction schemes to extract pronunciation and prosody representation of accented speech based on existing techniques in speech processing field. Correlation analysis on both segmental and suprasegmental feature space is conducted to look into the relationship between acoustic measurements related to L1s and perceived accentedness across several L1s. Multiple regression analysis is employed to investigate how the L1's effect impacts the perception of foreign accent, and how accented speech produced by speakers from different L1s behaves distinctly on segmental and suprasegmental feature spaces. Results unveil the potential application of the methodology in this study to provide quantitative analysis of accented speech, and extend current studies in L2 speech learning theory to large scale. Practically, this study further shows that the computational model proposed in this study can benefit automatic accentedness evaluation system by adding features related to speakers' L1s.Dissertation/ThesisDoctoral Dissertation Speech and Hearing Science 201

Phonological Level wav2vec2-based Mispronunciation Detection and Diagnosis Method

The automatic identification and analysis of pronunciation errors, known as Mispronunciation Detection and Diagnosis (MDD) plays a crucial role in Computer Aided Pronunciation Learning (CAPL) tools such as Second-Language (L2) learning or speech therapy applications. Existing MDD methods relying on analysing phonemes can only detect categorical errors of phonemes that have an adequate amount of training data to be modelled. With the unpredictable nature of the pronunciation errors of non-native or disordered speakers and the scarcity of training datasets, it is unfeasible to model all types of mispronunciations. Moreover, phoneme-level MDD approaches have a limited ability to provide detailed diagnostic information about the error made. In this paper, we propose a low-level MDD approach based on the detection of speech attribute features. Speech attribute features break down phoneme production into elementary components that are directly related to the articulatory system leading to more formative feedback to the learner. We further propose a multi-label variant of the Connectionist Temporal Classification (CTC) approach to jointly model the non-mutually exclusive speech attributes using a single model. The pre-trained wav2vec2 model was employed as a core model for the speech attribute detector. The proposed method was applied to L2 speech corpora collected from English learners from different native languages. The proposed speech attribute MDD method was further compared to the traditional phoneme-level MDD and achieved a significantly lower False Acceptance Rate (FAR), False Rejection Rate (FRR), and Diagnostic Error Rate (DER) over all speech attributes compared to the phoneme-level equivalent

Automatic Pronunciation Assessment -- A Review

Pronunciation assessment and its application in computer-aided pronunciation training (CAPT) have seen impressive progress in recent years. With the rapid growth in language processing and deep learning over the past few years, there is a need for an updated review. In this paper, we review methods employed in pronunciation assessment for both phonemic and prosodic. We categorize the main challenges observed in prominent research trends, and highlight existing limitations, and available resources. This is followed by a discussion of the remaining challenges and possible directions for future work.Comment: 9 pages, accepted to EMNLP Finding

자동발음평가-발음오류검출 통합 모델

학위논문(석사) -- 서울대학교대학원 : 인문대학 언어학과, 2023. 8. 정민화.실증 연구에 의하면 비원어민 발음 평가에 있어 전문 평가자가 채점하는 발음 점수와 음소 오류 사이의 상관관계는 매우 높다. 그러나 기존의 컴퓨터기반발음훈련 (Computer-assisted Pronunciation Training; CAPT) 시스템은 자동발음평가 (Automatic Pronunciation Assessment; APA) 과제 및 발음오류검출 (Mispronunciation Detection and Diagnosis; MDD) 과제를 독립적인 과제로 취급하며 각 모델의 성능을 개별적으로 향상시키는 것에만 초점을 두었다. 본 연구에서는 두 과제 사이의 높은 상관관계에 주목, 다중작업학습 기법을 활용하여 자동발음평가와 발음오류검출 과제를 동시에 훈련하는 새로운 아키텍처를 제안한다. 구체적으로는 APA 과제를 위해 교차 엔트로피 손실함수 및 RMSE 손실함수를 실험하며, MDD 손실함수는 CTC 손실함수로 고정된다. 근간 음향 모델은 사전훈련된 자기지도학습기반 모델로 하며, 이때 더욱 풍부한 음향 정보를 위해 다중작업학습을 거치기 전에 부수적으로 음소인식에 대하여 미세조정되기도 한다. 음향 모델과 함께 발음적합점수(Goodness-of-Pronunciation; GOP)가 추가적인 입력으로 사용된다. 실험 결과, 통합 모델이 단일 자동발음평가 및 발음오류검출 모델보다 매우 높은 성능을 보였다. 구체적으로는 Speechocean762 데이터셋에서 자동발음평가 과제에 사용된 네 항목의 점수들의 평균 피어슨상관계수가 0.041 증가하였으며, 발음오류검출 과제에 대해 F1 점수가 0.003 증가하였다. 통합 모델에 대해 시도된 아키텍처 중에서는, Robust Wav2vec2.0 음향모델과 발음적합점수를 활용하여 RMSE/CTC 손실함수로 훈련한 모델의 성능이 가장 좋았다. 모델을 분석한 결과, 통합 모델이 개별 모델에 비해 분포가 낮은 점수 및 발음오류를 더 정확하게 구분하였음을 확인할 수 있었다. 흥미롭게도 통합 모델에 있어 각 하위 과제들의 성능 향상 정도는 각 발음 점수와 발음 오류 레이블 사이의 상관계수 크기에 비례하였다. 또 통합 모델의 성능이 개선될수록 모델의 예측 발음점수, 그리고 모델의 예측 발음오류에 대한 상관성이 높아졌다. 본 연구 결과는 통합 모델이 발음 점수 및 음소 오류 사이의 언어학적 상관성을 활용하여 자동발음평가 및 발음오류검출 과제의 성능을 향상시켰으며, 그 결과 통합 모델이 전문 평가자들의 실제 비원어민 평가와 비슷한 양상을 띤다는 것을 보여준다.Empirical studies report a strong correlation between pronunciation scores and mispronunciations in non-native speech assessments of human evaluators. However, the existing system of computer-assisted pronunciation training (CAPT) regards automatic pronunciation assessment (APA) and mispronunciation detection and diagnosis (MDD) as independent and focuses on individual performance improvement. Motivated by the correlation between two tasks, this study proposes a novel architecture that jointly tackles APA and MDD with a multi-task learning scheme to benefit both tasks. Specifically, APA loss is examined between cross-entropy and root mean square error (RMSE) criteria, and MDD loss is fixed to Connectionist Temporal Classification (CTC) criteria. For the backbone acoustic model, self-supervised model is used with an auxiliary fine-tuning on phone recognition before multi-task learning to leverage extra knowledge transfer. Goodness-of-Pronunciation (GOP) measure is given as an additional input along with the acoustic model. The joint model significantly outperformed single-task learning counterparts, with a mean of 0.041 PCC increase for APA task on four multi-aspect scores and 0.003 F1 increase for MDD task on Speechocean762 dataset. For the joint model architecture, multi-task learning with RMSE and CTC criteria with raw Robust Wav2vec2.0 and GOP measure achieved the best performance. Analysis indicates that the joint model learned to distinguish scores with low distribution, and to better recognize mispronunciations as mispronunciations compared to single-task learning models. Interestingly, the degree of the performance increase in each subtask for the joint model was proportional to the strength of the correlation between respective pronunciation score and mispronunciation labels, and the strength of the correlation between the model predictions also increased as the joint model achieved higher performances. The findings reveal that the joint model leveraged the linguistic correlation between pronunciation scores and mispronunciations to improve performances for APA and MDD tasks, and to show behaviors that follow the assessments of human experts.Chapter 1, Introduction 1 Chapter 2. Related work 5 Chapter 3. Methodology 17 Chapter 4. Results 28 Chapter 5. Discussion 47 Chapter 6. Conclusion 52 References 53 Appendix 60 국문 초록 65석

Leveraging phone-level linguistic-acoustic similarity for utterance-level pronunciation scoring

Recent studies on pronunciation scoring have explored the effect of introducing phone embeddings as reference pronunciation, but mostly in an implicit manner, i.e., addition or concatenation of reference phone embedding and actual pronunciation of the target phone as the phone-level pronunciation quality representation. In this paper, we propose to use linguistic-acoustic similarity to explicitly measure the deviation of non-native production from its native reference for pronunciation assessment. Specifically, the deviation is first estimated by the cosine similarity between reference phone embedding and corresponding acoustic embedding. Next, a phone-level Goodness of pronunciation (GOP) pre-training stage is introduced to guide this similarity-based learning for better initialization of the aforementioned two embeddings. Finally, a transformer-based hierarchical pronunciation scorer is used to map a sequence of phone embeddings, acoustic embeddings along with their similarity measures to predict the final utterance-level score. Experimental results on the non-native databases suggest that the proposed system significantly outperforms the baselines, where the acoustic and phone embeddings are simply added or concatenated. A further examination shows that the phone embeddings learned in the proposed approach are able to capture linguistic-acoustic attributes of native pronunciation as reference.Comment: Accepted by ICASSP 202

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

Artificial Neural Network (ANN) in a Small Dataset to determine Neutrality in the Pronunciation of English as a Foreign Language in Filipino Call Center Agents

Artificial Neural Networks (ANNs) have continued to be efficient models in solving classification problems. In this paper, we explore the use of an ANN with a small dataset to accurately classify whether Filipino call center agents’ pronunciations are neutral or not based on their employer’s standards. Isolated utterances of the ten most commonly used words in the call center were recorded from eleven agents creating a dataset of 110 utterances. Two learning specialists were consulted to establish ground truths and Cohen’s Kappa was computed as 0.82, validating the reliability of the dataset. The first thirteen Mel-Frequency Cepstral Coefficients (MFCCs) were then extracted from each word and an ANN was trained with Ten-fold Stratified Cross Validation. Experimental results on the model recorded a classification accuracy of 89.60% supported by an overall F-Score of 0.92

MISPRONUNCIATION DETECTION AND DIAGNOSIS IN MANDARIN ACCENTED ENGLISH SPEECH

This work presents the development, implementation, and evaluation of a Mispronunciation Detection and Diagnosis (MDD) system, with application to pronunciation evaluation of Mandarin-accented English speech. A comprehensive detection and diagnosis of errors in the Electromagnetic Articulography corpus of Mandarin-Accented English (EMA-MAE) was performed by using the expert phonetic transcripts and an Automatic Speech Recognition (ASR) system. Articulatory features derived from the parallel kinematic data available in the EMA-MAE corpus were used to identify the most significant articulatory error patterns seen in L2 speakers during common mispronunciations. Using both acoustic and articulatory information, an ASR based Mispronunciation Detection and Diagnosis (MDD) system was built and evaluated across different feature combinations and Deep Neural Network (DNN) architectures. The MDD system captured mispronunciation errors with a detection accuracy of 82.4%, a diagnostic accuracy of 75.8% and a false rejection rate of 17.2%. The results demonstrate the advantage of using articulatory features in revealing the significant contributors of mispronunciation as well as improving the performance of MDD systems