Self-imitating Feedback Generation Using GAN for Computer-Assisted Pronunciation Training

Self-imitating feedback is an effective and learner-friendly method for non-native learners in Computer-Assisted Pronunciation Training. Acoustic characteristics in native utterances are extracted and transplanted onto learner's own speech input, and given back to the learner as a corrective feedback. Previous works focused on speech conversion using prosodic transplantation techniques based on PSOLA algorithm. Motivated by the visual differences found in spectrograms of native and non-native speeches, we investigated applying GAN to generate self-imitating feedback by utilizing generator's ability through adversarial training. Because this mapping is highly under-constrained, we also adopt cycle consistency loss to encourage the output to preserve the global structure, which is shared by native and non-native utterances. Trained on 97,200 spectrogram images of short utterances produced by native and non-native speakers of Korean, the generator is able to successfully transform the non-native spectrogram input to a spectrogram with properties of self-imitating feedback. Furthermore, the transformed spectrogram shows segmental corrections that cannot be obtained by prosodic transplantation. Perceptual test comparing the self-imitating and correcting abilities of our method with the baseline PSOLA method shows that the generative approach with cycle consistency loss is promising

Imitation/self-imitation in computer-assisted prosody training for Chinese learners of L2 Italian.

Recent studies on L2 acquisition, speech synthesis and automatic identification of foreign accents argue for a major role of prosody in the perception of non-native speech. Research on the relationship between pronunciation improvement and student/teachers’ voice similarities has also shown that the better the match between the learners' and native speakers' voices in terms of f0 and articulation rate, the more positive the impact on pronunciation training. This study investigates the effects of imitation and self-imitation on the acquisition of L2 suprasegmental patterns. Degree of foreign accent, improvements in intelligibility, and effectiveness of communication were measured to determine the success of each technique. For this purpose, a prosodic transplantation technique and a computer-assisted learning methodology were used. Recent studies on L2 acquisition, speech synthesis and automatic identification of foreign accents argue for a major role of prosody in the perception of non-native speech. Research on the relationship between pronunciation improvement and student/teachers’ voice similarities has also shown that the better the match between the learners' and native speakers' voices in terms of f0 and articulation rate, the more positive the impact on pronunciation training. This study investigates the effects of imitation and self-imitation on the acquisition of L2 suprasegmental patterns. Degree of foreign accent, improvements in intelligibility, and effectiveness of communication were measured to determine the success of each technique. For this purpose, a prosodic transplantation technique and a computer-assisted learning methodology were used

L2-ARCTIC: A Non-Native English Speech Corpus

In this paper, we introduce L2-ARCTIC, a speech corpus of non-native English that is intended for research in voice conversion, accent conversion, and mispronunciation detection. This initial release includes recordings from ten non-native speakers of English whose first languages (L1s) are Hindi, Korean, Mandarin, Spanish, and Arabic, each L1 containing recordings from one male and one female speaker. Each speaker recorded approximately one hour of read speech from the Carnegie Mellon University ARCTIC prompts, from which we generated orthographic and forced-aligned phonetic transcriptions. In addition, we manually annotated 150 utterances per speaker to identify three types of mispronunciation errors: substitutions, deletions, and additions, making it a valuable resource not only for research in voice conversion and accent conversion but also in computer-assisted pronunciation training. The corpus is publicly accessible at https://psi.engr.tamu.edu/l2-arctic-corpus/

Design and analysis of a database to evaluate children’s reading aloud performance

To evaluate the reading performance of children, human assessment is usually involved, where a teacher or tutor has to take time to individually estimate the performance in terms of fluency (speed, accuracy and expression). Automatic estimation of reading ability can be an important alternative or complement to the usual methods, and can improve other applications such as e-learning. Techniques must be developed to analyse audio recordings of read utterances by children and detect the deviations from the intended correct reading i.e. disfluencies. For that goal, a database of 284 European Portuguese children from 6 to 10 years old (1st–4th grades) reading aloud amounting to 20 h was collected in private and public Portuguese schools. This paper describes the design of the reading tasks as well as the data collection procedure. The presence of different types of disfluencies is analysed as well as reading performance compared to known curricular goals.info:eu-repo/semantics/acceptedVersio

After Self-Imitation Prosodic Training L2 Learners Converge Prosodically to the Native Speakers

Little attention is paid to prosody in second language (L2) instruction, but computer-assisted pronunciation training (CAPT) offers learners solutions to improve the perception and production of L2 suprasegmentals. In this study, we extend with acoustic analysis a previous research showing the effectiveness of self-imitation training on prosodic improvements of Japanese learners of Italian. In light of the increased degree of correct match between intended and perceived pragmatic functions (e.g., speech acts), in this study, we aimed at quantifying the degree of prosodic convergence towards L1 Italian speakers used as a model for self-imitation training. To measure convergence, we calculated the difference in duration, F0 mean, and F0 max syllable-wise between L1 utterances and the corresponding L2 utterances produced before and after training. The results showed that after self-imitation training, L2 learners converged to the L1 speakers. The extent of the effect, however, varied based on the speech act, the acoustic measure, and the distance between L1 and L2 speakers before the training. The findings from perceptual and acoustic investigations, taken together, show the potential of self-imitation prosodic training as a valuable tool to help L2 learners communicate more effectively

Tongue control and its implication in pronunciation training

International audiencePronunciation training based on speech production techniques illustrating tongue movements is gaining popularity. However, there is not sufficient evidence that learners can imitate some tongue animation. In this paper, we argue that although controlling tongue movement related to speech is not such an easy task, training with visual feedback improves its control. We investigated human awareness of controlling their tongue body gestures. In a first experiment, participants were asked to perform some tongue movements composed of two sets of gestures. This task was evaluated by observing ultrasound imaging of the tongue recorded during the experiment. No feedback was provided. In a second experiment, a short session of training was added where participants can observe ultrasound imaging in real time of their own tongue movements. The goal was to increase their awareness of their tongue gestures. A pretest and posttest were carried out without any feedback. The results suggest that without a priori knowledge, it is not easy to finely control tongue body gestures. The second experiment showed that we gained in performance after a short training session and this suggests that providing visual feedback, even a short one, improves tongue gesture awareness

“A better me”: Using acoustically modified learner voices as models

This paper presents the results of a brief mixed-methods intervention which sought to modify the production of prominence-related features in L2 English by four native French-speaking university lecturers, in read-aloud speech. Selected parts of participants’ productions were acoustically modified and then used as the model in a Listen-and-Repeat protocol, where both quantitative (acoustic measures) and qualitative (free comments from discussion) data were collected. Acoustic measures were taken again from productions realized three months after the protocol, to trace longer term retention of modifications; expert listeners compared a selection of these productions to the original, diagnostic renditions, rating the degree of native-like rhythm and melody. Analysis of the quantitative and qualitative results confirm that imitating oneself can help individuals to modify prominence-related features of their pronunciation, that such changes can be retained over a 3-month period, but that people cannot reliably judge what they have modified. New potential is thus shown for Listen-and-Repeat, using one’s own modified voice, as an effective technique in pronunciation instruction

CAPT를 위한 발음 변이 분석 및 CycleGAN 기반 피드백 생성

학위논문(박사)--서울대학교 대학원 :인문대학 협동과정 인지과학전공,2020. 2. 정민화.Despite the growing popularity in learning Korean as a foreign language and the rapid development in language learning applications, the existing computer-assisted pronunciation training (CAPT) systems in Korean do not utilize linguistic characteristics of non-native Korean speech. Pronunciation variations in non-native speech are far more diverse than those observed in native speech, which may pose a difficulty in combining such knowledge in an automatic system. Moreover, most of the existing methods rely on feature extraction results from signal processing, prosodic analysis, and natural language processing techniques. Such methods entail limitations since they necessarily depend on finding the right features for the task and the extraction accuracies. This thesis presents a new approach for corrective feedback generation in a CAPT system, in which pronunciation variation patterns and linguistic correlates with accentedness are analyzed and combined with a deep neural network approach, so that feature engineering efforts are minimized while maintaining the linguistically important factors for the corrective feedback generation task. Investigations on non-native Korean speech characteristics in contrast with those of native speakers, and their correlation with accentedness judgement show that both segmental and prosodic variations are important factors in a Korean CAPT system. The present thesis argues that the feedback generation task can be interpreted as a style transfer problem, and proposes to evaluate the idea using generative adversarial network. A corrective feedback generation model is trained on 65,100 read utterances by 217 non-native speakers of 27 mother tongue backgrounds. The features are automatically learnt in an unsupervised way in an auxiliary classifier CycleGAN setting, in which the generator learns to map a foreign accented speech to native speech distributions. In order to inject linguistic knowledge into the network, an auxiliary classifier is trained so that the feedback also identifies the linguistic error types that were defined in the first half of the thesis. The proposed approach generates a corrected version the speech using the learners own voice, outperforming the conventional Pitch-Synchronous Overlap-and-Add method.외국어로서의 한국어 교육에 대한 관심이 고조되어 한국어 학습자의 수가 크게 증가하고 있으며, 음성언어처리 기술을 적용한 컴퓨터 기반 발음 교육(Computer-Assisted Pronunciation Training; CAPT) 어플리케이션에 대한 연구 또한 적극적으로 이루어지고 있다. 그럼에도 불구하고 현존하는 한국어 말하기 교육 시스템은 외국인의 한국어에 대한 언어학적 특징을 충분히 활용하지 않고 있으며, 최신 언어처리 기술 또한 적용되지 않고 있는 실정이다. 가능한 원인으로써는 외국인 발화 한국어 현상에 대한 분석이 충분하게 이루어지지 않았다는 점, 그리고 관련 연구가 있어도 이를 자동화된 시스템에 반영하기에는 고도화된 연구가 필요하다는 점이 있다. 뿐만 아니라 CAPT 기술 전반적으로는 신호처리, 운율 분석, 자연어처리 기법과 같은 특징 추출에 의존하고 있어서 적합한 특징을 찾고 이를 정확하게 추출하는 데에 많은 시간과 노력이 필요한 실정이다. 이는 최신 딥러닝 기반 언어처리 기술을 활용함으로써 이 과정 또한 발전의 여지가 많다는 바를 시사한다. 따라서 본 연구는 먼저 CAPT 시스템 개발에 있어 발음 변이 양상과 언어학적 상관관계를 분석하였다. 외국인 화자들의 낭독체 변이 양상과 한국어 원어민 화자들의 낭독체 변이 양상을 대조하고 주요한 변이를 확인한 후, 상관관계 분석을 통하여 의사소통에 영향을 미치는 중요도를 파악하였다. 그 결과, 종성 삭제와 3중 대립의 혼동, 초분절 관련 오류가 발생할 경우 피드백 생성에 우선적으로 반영하는 것이 필요하다는 것이 확인되었다. 교정된 피드백을 자동으로 생성하는 것은 CAPT 시스템의 중요한 과제 중 하나이다. 본 연구는 이 과제가 발화의 스타일 변화의 문제로 해석이 가능하다고 보았으며, 생성적 적대 신경망 (Cycle-consistent Generative Adversarial Network; CycleGAN) 구조에서 모델링하는 것을 제안하였다. GAN 네트워크의 생성모델은 비원어민 발화의 분포와 원어민 발화 분포의 매핑을 학습하며, Cycle consistency 손실함수를 사용함으로써 발화간 전반적인 구조를 유지함과 동시에 과도한 교정을 방지하였다. 별도의 특징 추출 과정이 없이 필요한 특징들이 CycleGAN 프레임워크에서 무감독 방법으로 스스로 학습되는 방법으로, 언어 확장이 용이한 방법이다. 언어학적 분석에서 드러난 주요한 변이들 간의 우선순위는 Auxiliary Classifier CycleGAN 구조에서 모델링하는 것을 제안하였다. 이 방법은 기존의 CycleGAN에 지식을 접목시켜 피드백 음성을 생성함과 동시에 해당 피드백이 어떤 유형의 오류인지 분류하는 문제를 수행한다. 이는 도메인 지식이 교정 피드백 생성 단계까지 유지되고 통제가 가능하다는 장점이 있다는 데에 그 의의가 있다. 본 연구에서 제안한 방법을 평가하기 위해서 27개의 모국어를 갖는 217명의 유의미 어휘 발화 65,100개로 피드백 자동 생성 모델을 훈련하고, 개선 여부 및 정도에 대한 지각 평가를 수행하였다. 제안된 방법을 사용하였을 때 학습자 본인의 목소리를 유지한 채 교정된 발음으로 변환하는 것이 가능하며, 전통적인 방법인 음높이 동기식 중첩가산 (Pitch-Synchronous Overlap-and-Add) 알고리즘을 사용하는 방법에 비해 상대 개선률 16.67%이 확인되었다.Chapter 1. Introduction 1 1.1. Motivation 1 1.1.1. An Overview of CAPT Systems 3 1.1.2. Survey of existing Korean CAPT Systems 5 1.2. Problem Statement 7 1.3. Thesis Structure 7 Chapter 2. Pronunciation Analysis of Korean Produced by Chinese 9 2.1. Comparison between Korean and Chinese 11 2.1.1. Phonetic and Syllable Structure Comparisons 11 2.1.2. Phonological Comparisons 14 2.2. Related Works 16 2.3. Proposed Analysis Method 19 2.3.1. Corpus 19 2.3.2. Transcribers and Agreement Rates 22 2.4. Salient Pronunciation Variations 22 2.4.1. Segmental Variation Patterns 22 2.4.1.1. Discussions 25 2.4.2. Phonological Variation Patterns 26 2.4.1.2. Discussions 27 2.5. Summary 29 Chapter 3. Correlation Analysis of Pronunciation Variations and Human Evaluation 30 3.1. Related Works 31 3.1.1. Criteria used in L2 Speech 31 3.1.2. Criteria used in L2 Korean Speech 32 3.2. Proposed Human Evaluation Method 36 3.2.1. Reading Prompt Design 36 3.2.2. Evaluation Criteria Design 37 3.2.3. Raters and Agreement Rates 40 3.3. Linguistic Factors Affecting L2 Korean Accentedness 41 3.3.1. Pearsons Correlation Analysis 41 3.3.2. Discussions 42 3.3.3. Implications for Automatic Feedback Generation 44 3.4. Summary 45 Chapter 4. Corrective Feedback Generation for CAPT 46 4.1. Related Works 46 4.1.1. Prosody Transplantation 47 4.1.2. Recent Speech Conversion Methods 49 4.1.3. Evaluation of Corrective Feedback 50 4.2. Proposed Method: Corrective Feedback as a Style Transfer 51 4.2.1. Speech Analysis at Spectral Domain 53 4.2.2. Self-imitative Learning 55 4.2.3. An Analogy: CAPT System and GAN Architecture 57 4.3. Generative Adversarial Networks 59 4.3.1. Conditional GAN 61 4.3.2. CycleGAN 62 4.4. Experiment 63 4.4.1. Corpus 64 4.4.2. Baseline Implementation 65 4.4.3. Adversarial Training Implementation 65 4.4.4. Spectrogram-to-Spectrogram Training 66 4.5. Results and Evaluation 69 4.5.1. Spectrogram Generation Results 69 4.5.2. Perceptual Evaluation 70 4.5.3. Discussions 72 4.6. Summary 74 Chapter 5. Integration of Linguistic Knowledge in an Auxiliary Classifier CycleGAN for Feedback Generation 75 5.1. Linguistic Class Selection 75 5.2. Auxiliary Classifier CycleGAN Design 77 5.3. Experiment and Results 80 5.3.1. Corpus 80 5.3.2. Feature Annotations 81 5.3.3. Experiment Setup 81 5.3.4. Results 82 5.4. Summary 84 Chapter 6. Conclusion 86 6.1. Thesis Results 86 6.2. Thesis Contributions 88 6.3. Recommendations for Future Work 89 Bibliography 91 Appendix 107 Abstract in Korean 117 Acknowledgments 120Docto

자동 운율 복제를 위한 모음 길이와 기본 주파수 예측

학위논문 (석사)-- 서울대학교 대학원 : 인문대학 협동과정 인지과학전공, 2018. 8. 정민화.The use of computers to help people improve their pronunciation skills of a foreign language has rapidly increased in the last decades. Majority of such Computer-Assisted Pronunciation Training (CAPT) systems have been focused on teaching correct pronunciation of segments only, however, while prosody received much less attention. One of the new approaches to prosody training is self-imitation learning. Prosodic features from a native utterance are transplanted onto learners own speech, and given back as corrective feedback. The main drawback is that this technique requires two identical sets of native and non-native utterances, which makes its actual implementation cumbersome and inflexible. As a preliminary research towards developing a new method of prosody transplantation, the first part of the study surveys previous related works and points out their advantages and drawbacks. We also compare prosodic systems of Korean and English, point out major areas of mistakes that Korean learners of English tend to do, and then we analyze acoustic features that this mistakes are correlated with. We suggest that transplantation of vowel duration and fundamental frequency will be the most effective for self-imitation learning by Korean speakers of English. The second part of this study introduces a new proposed model for prosody transplantation. Instead of transplanting acoustic values from a pre-recorded utterance, we suggest to use a deep neural network (DNN) based system to predict them instead. Three different models are built and described: baseline recurrent neural network (RNN), long short-term memory (LSTM) model and gated recurrent unit (GRU) model. The models were trained on Boston University Radio Speech Corpus, using a minimal set of relevant input features. The models were compared with each other, as well as with state-of-the-art prosody prediction systems from speech synthesis research. Implementation of the proposed prediction model in automatic prosody transplantation is described and the results are analyzed. A perceptual evaluation by native speakers was carried out. Accentedness and comprehensibility ratings of modified and original non-native utterances were compared with each other. The results showed that duration transplantation can lead to the improvements in comprehensibility score. This study lays the groundwork for a fully automatic self-imitation prosody training system and its results can be used to help Korean learners master problematic areas of English prosody, such as sentence stress.Chapter 1. Introduction . 10 1.1 Background. 10 1.2 Research Objective 12 1.3 Research Outline. 15 Chapter 2. Related Works. 16 2.1 Self-imitation Prosody Training. 16 2.1.1 Prosody Transplantation Methods . 18 2.1.2 Effects of Prosody Transplantation on Accentedness Rating 23 2.1.3 Effects of Self-Imitation Learning on Proficiency Rating 26 2.2 Prosody of Korean-accented English Speech 28 2.2.1 Prosodic Systems of Korean and English 28 2.2.2 Common Prosodic Mistakes. 29 2.3 Deep Learning Based Prosody Prediction 34 2.3.1 Deep Learning . 34 2.3.2 Recurrent Neural Networks 35 2.3.2 The Long Short-Term Memory Architecture. 37 2.3.3 Gated Recurrent Units. 39 2.3.4 Prosody Prediction Models 40 Chapter 3. Vowel Duration and Fundamental Frequency Prediction Model 43 3.1 Data 43 3.2. Input Feature Selection. 45 3.3 System Architecture and Training 56 3.4 Results and Evaluation 63 3.4.1 Objective Metrics. 63 3.4.2 Vowel Duration Prediction Models Results. 65 3.4.2 Fundamental Frequency Prediction Models Results 68 3.4.3 Comparison with other models . 68 Chapter 4. Automatic Prosody Transplantation 72 4.1 Data 72 4.2 Transplantation Method. 74 4.3 Perceptual Evaluation 79 4.4 Results 80 Chapter 5. Conclusion. 82 5.1 Summary 82 5.2 Contribution 84 5.3 Limitations 85 5.4 Recommendations for Future Study. 85 References 88 Appendix 96Maste