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Rhythm-Flexible Voice Conversion without Parallel Data Using Cycle-GAN over Phoneme Posteriorgram Sequences
Speaking rate refers to the average number of phonemes within some unit time,
while the rhythmic patterns refer to duration distributions for realizations of
different phonemes within different phonetic structures. Both are key
components of prosody in speech, which is different for different speakers.
Models like cycle-consistent adversarial network (Cycle-GAN) and variational
auto-encoder (VAE) have been successfully applied to voice conversion tasks
without parallel data. However, due to the neural network architectures and
feature vectors chosen for these approaches, the length of the predicted
utterance has to be fixed to that of the input utterance, which limits the
flexibility in mimicking the speaking rates and rhythmic patterns for the
target speaker. On the other hand, sequence-to-sequence learning model was used
to remove the above length constraint, but parallel training data are needed.
In this paper, we propose an approach utilizing sequence-to-sequence model
trained with unsupervised Cycle-GAN to perform the transformation between the
phoneme posteriorgram sequences for different speakers. In this way, the length
constraint mentioned above is removed to offer rhythm-flexible voice conversion
without requiring parallel data. Preliminary evaluation on two datasets showed
very encouraging results.Comment: 8 pages, 6 figures, Submitted to SLT 201
CAPTλ₯Ό μν λ°μ λ³μ΄ λΆμ λ° CycleGAN κΈ°λ° νΌλλ°± μμ±
νμλ
Όλ¬Έ(λ°μ¬)--μμΈλνκ΅ λνμ :μΈλ¬Έλν νλκ³Όμ μΈμ§κ³Όνμ 곡,2020. 2. μ λ―Όν.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.μΈκ΅μ΄λ‘μμ νκ΅μ΄ κ΅μ‘μ λν κ΄μ¬μ΄ κ³ μ‘°λμ΄ νκ΅μ΄ νμ΅μμ μκ° ν¬κ² μ¦κ°νκ³ μμΌλ©°, μμ±μΈμ΄μ²λ¦¬ κΈ°μ μ μ μ©ν μ»΄ν¨ν° κΈ°λ° λ°μ κ΅μ‘(Computer-Assisted Pronunciation Training; CAPT) μ΄ν리μΌμ΄μ
μ λν μ°κ΅¬ λν μ κ·Ήμ μΌλ‘ μ΄λ£¨μ΄μ§κ³ μλ€. κ·ΈλΌμλ λΆκ΅¬νκ³ νμ‘΄νλ νκ΅μ΄ λ§νκΈ° κ΅μ‘ μμ€ν
μ μΈκ΅μΈμ νκ΅μ΄μ λν μΈμ΄νμ νΉμ§μ μΆ©λΆν νμ©νμ§ μκ³ μμΌλ©°, μ΅μ μΈμ΄μ²λ¦¬ κΈ°μ λν μ μ©λμ§ μκ³ μλ μ€μ μ΄λ€. κ°λ₯ν μμΈμΌλ‘μ¨λ μΈκ΅μΈ λ°ν νκ΅μ΄ νμμ λν λΆμμ΄ μΆ©λΆνκ² μ΄λ£¨μ΄μ§μ§ μμλ€λ μ , κ·Έλ¦¬κ³ κ΄λ ¨ μ°κ΅¬κ° μμ΄λ μ΄λ₯Ό μλνλ μμ€ν
μ λ°μνκΈ°μλ κ³ λνλ μ°κ΅¬κ° νμνλ€λ μ μ΄ μλ€. λΏλ§ μλλΌ CAPT κΈ°μ μ λ°μ μΌλ‘λ μ νΈμ²λ¦¬, μ΄μ¨ λΆμ, μμ°μ΄μ²λ¦¬ κΈ°λ²κ³Ό κ°μ νΉμ§ μΆμΆμ μμ‘΄νκ³ μμ΄μ μ ν©ν νΉμ§μ μ°Ύκ³ μ΄λ₯Ό μ ννκ² μΆμΆνλ λ°μ λ§μ μκ°κ³Ό λ
Έλ ₯μ΄ νμν μ€μ μ΄λ€. μ΄λ μ΅μ λ₯λ¬λ κΈ°λ° μΈμ΄μ²λ¦¬ κΈ°μ μ νμ©ν¨μΌλ‘μ¨ μ΄ κ³Όμ λν λ°μ μ μ¬μ§κ° λ§λ€λ λ°λ₯Ό μμ¬νλ€.
λ°λΌμ λ³Έ μ°κ΅¬λ λ¨Όμ CAPT μμ€ν
κ°λ°μ μμ΄ λ°μ λ³μ΄ μμκ³Ό μΈμ΄νμ μκ΄κ΄κ³λ₯Ό λΆμνμλ€. μΈκ΅μΈ νμλ€μ λλ
체 λ³μ΄ μμκ³Ό νκ΅μ΄ μμ΄λ―Ό νμλ€μ λλ
체 λ³μ΄ μμμ λμ‘°νκ³ μ£Όμν λ³μ΄λ₯Ό νμΈν ν, μκ΄κ΄κ³ λΆμμ ν΅νμ¬ μμ¬μν΅μ μν₯μ λ―ΈμΉλ μ€μλλ₯Ό νμ
νμλ€. κ·Έ κ²°κ³Ό, μ’
μ± μμ μ 3μ€ λ립μ νΌλ, μ΄λΆμ κ΄λ ¨ μ€λ₯κ° λ°μν κ²½μ° νΌλλ°± μμ±μ μ°μ μ μΌλ‘ λ°μνλ κ²μ΄ νμνλ€λ κ²μ΄ νμΈλμλ€.
κ΅μ λ νΌλλ°±μ μλμΌλ‘ μμ±νλ κ²μ CAPT μμ€ν
μ μ€μν κ³Όμ μ€ νλμ΄λ€. λ³Έ μ°κ΅¬λ μ΄ κ³Όμ κ° λ°νμ μ€νμΌ λ³νμ λ¬Έμ λ‘ ν΄μμ΄ κ°λ₯νλ€κ³ 보μμΌλ©°, μμ±μ μ λ μ κ²½λ§ (Cycle-consistent Generative Adversarial Network; CycleGAN) ꡬ쑰μμ λͺ¨λΈλ§νλ κ²μ μ μνμλ€. GAN λ€νΈμν¬μ μμ±λͺ¨λΈμ λΉμμ΄λ―Ό λ°νμ λΆν¬μ μμ΄λ―Ό λ°ν λΆν¬μ 맀νμ νμ΅νλ©°, Cycle consistency μμ€ν¨μλ₯Ό μ¬μ©ν¨μΌλ‘μ¨ λ°νκ° μ λ°μ μΈ κ΅¬μ‘°λ₯Ό μ μ§ν¨κ³Ό λμμ κ³Όλν κ΅μ μ λ°©μ§νμλ€. λ³λμ νΉμ§ μΆμΆ κ³Όμ μ΄ μμ΄ νμν νΉμ§λ€μ΄ CycleGAN νλ μμν¬μμ 무κ°λ
λ°©λ²μΌλ‘ μ€μ€λ‘ νμ΅λλ λ°©λ²μΌλ‘, μΈμ΄ νμ₯μ΄ μ©μ΄ν λ°©λ²μ΄λ€.
μΈμ΄νμ λΆμμμ λλ¬λ μ£Όμν λ³μ΄λ€ κ°μ μ°μ μμλ Auxiliary Classifier CycleGAN ꡬ쑰μμ λͺ¨λΈλ§νλ κ²μ μ μνμλ€. μ΄ λ°©λ²μ κΈ°μ‘΄μ CycleGANμ μ§μμ μ λͺ©μμΌ νΌλλ°± μμ±μ μμ±ν¨κ³Ό λμμ ν΄λΉ νΌλλ°±μ΄ μ΄λ€ μ νμ μ€λ₯μΈμ§ λΆλ₯νλ λ¬Έμ λ₯Ό μννλ€. μ΄λ λλ©μΈ μ§μμ΄ κ΅μ νΌλλ°± μμ± λ¨κ³κΉμ§ μ μ§λκ³ ν΅μ κ° κ°λ₯νλ€λ μ₯μ μ΄ μλ€λ λ°μ κ·Έ μμκ° μλ€.
λ³Έ μ°κ΅¬μμ μ μν λ°©λ²μ νκ°νκΈ° μν΄μ 27κ°μ λͺ¨κ΅μ΄λ₯Ό κ°λ 217λͺ
μ μ μλ―Έ μ΄ν λ°ν 65,100κ°λ‘ νΌλλ°± μλ μμ± λͺ¨λΈμ νλ ¨νκ³ , κ°μ μ¬λΆ λ° μ λμ λν μ§κ° νκ°λ₯Ό μννμλ€. μ μλ λ°©λ²μ μ¬μ©νμμ λ νμ΅μ λ³ΈμΈμ λͺ©μ리λ₯Ό μ μ§ν μ± κ΅μ λ λ°μμΌλ‘ λ³ννλ κ²μ΄ κ°λ₯νλ©°, μ ν΅μ μΈ λ°©λ²μΈ μλμ΄ λκΈ°μ μ€μ²©κ°μ° (Pitch-Synchronous Overlap-and-Add) μκ³ λ¦¬μ¦μ μ¬μ©νλ λ°©λ²μ λΉν΄ μλ κ°μ λ₯ 16.67%μ΄ νμΈλμλ€.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
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