2,352 research outputs found
Multilingual Adaptation of RNN Based ASR Systems
In this work, we focus on multilingual systems based on recurrent neural
networks (RNNs), trained using the Connectionist Temporal Classification (CTC)
loss function. Using a multilingual set of acoustic units poses difficulties.
To address this issue, we proposed Language Feature Vectors (LFVs) to train
language adaptive multilingual systems. Language adaptation, in contrast to
speaker adaptation, needs to be applied not only on the feature level, but also
to deeper layers of the network. In this work, we therefore extended our
previous approach by introducing a novel technique which we call "modulation".
Based on this method, we modulated the hidden layers of RNNs using LFVs. We
evaluated this approach in both full and low resource conditions, as well as
for grapheme and phone based systems. Lower error rates throughout the
different conditions could be achieved by the use of the modulation.Comment: 5 pages, 1 figure, to appear in 2018 IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP 2018
Transfer Learning for Speech and Language Processing
Transfer learning is a vital technique that generalizes models trained for
one setting or task to other settings or tasks. For example in speech
recognition, an acoustic model trained for one language can be used to
recognize speech in another language, with little or no re-training data.
Transfer learning is closely related to multi-task learning (cross-lingual vs.
multilingual), and is traditionally studied in the name of `model adaptation'.
Recent advance in deep learning shows that transfer learning becomes much
easier and more effective with high-level abstract features learned by deep
models, and the `transfer' can be conducted not only between data distributions
and data types, but also between model structures (e.g., shallow nets and deep
nets) or even model types (e.g., Bayesian models and neural models). This
review paper summarizes some recent prominent research towards this direction,
particularly for speech and language processing. We also report some results
from our group and highlight the potential of this very interesting research
field.Comment: 13 pages, APSIPA 201
Content-Dependent Fine-Grained Speaker Embedding for Zero-Shot Speaker Adaptation in Text-to-Speech Synthesis
Zero-shot speaker adaptation aims to clone an unseen speaker's voice without
any adaptation time and parameters. Previous researches usually use a speaker
encoder to extract a global fixed speaker embedding from reference speech, and
several attempts have tried variable-length speaker embedding. However, they
neglect to transfer the personal pronunciation characteristics related to
phoneme content, leading to poor speaker similarity in terms of detailed
speaking styles and pronunciation habits. To improve the ability of the speaker
encoder to model personal pronunciation characteristics, we propose
content-dependent fine-grained speaker embedding for zero-shot speaker
adaptation. The corresponding local content embeddings and speaker embeddings
are extracted from a reference speech, respectively. Instead of modeling the
temporal relations, a reference attention module is introduced to model the
content relevance between the reference speech and the input text, and to
generate the fine-grained speaker embedding for each phoneme encoder output.
The experimental results show that our proposed method can improve speaker
similarity of synthesized speeches, especially for unseen speakers.Comment: Submitted to Interspeech 202
Text-to-speech system for low-resource language using cross-lingual transfer learning and data augmentation
Deep learning techniques are currently being applied in automated text-to-speech (TTS) systems, resulting in significant improvements in performance. However, these methods require large amounts of text-speech paired data for model training, and collecting this data is costly. Therefore, in this paper, we propose a single-speaker TTS system containing both a spectrogram prediction network and a neural vocoder for the target language, using only 30 min of target language text-speech paired data for training. We evaluate three approaches for training the spectrogram prediction models of our TTS system, which produce mel-spectrograms from the input phoneme sequence: (1) cross-lingual transfer learning, (2) data augmentation, and (3) a combination of the previous two methods. In the cross-lingual transfer learning method, we used two high-resource language datasets, English (24 h) and Japanese (10 h). We also used 30 min of target language data for training in all three approaches, and for generating the augmented data used for training in methods 2 and 3. We found that using both cross-lingual transfer learning and augmented data during training resulted in the most natural synthesized target speech output. We also compare single-speaker and multi-speaker training methods, using sequential and simultaneous training, respectively. The multi-speaker models were found to be more effective for constructing a single-speaker, low-resource TTS model. In addition, we trained two Parallel WaveGAN (PWG) neural vocoders, one using 13 h of our augmented data with 30 min of target language data and one using the entire 12 h of the original target language dataset. Our subjective AB preference test indicated that the neural vocoder trained with augmented data achieved almost the same perceived speech quality as the vocoder trained with the entire target language dataset. Overall, we found that our proposed TTS system consisting of a spectrogram prediction network and a PWG neural vocoder was able to achieve reasonable performance using only 30 min of target language training data. We also found that by using 3 h of target language data, for training the model and for generating augmented data, our proposed TTS model was able to achieve performance very similar to that of the baseline model, which was trained with 12 h of target language data
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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