제어 가능한 음성 합성을 위한 게이트 재귀 어텐션과 다변수 정보 최소화

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2021.8. 천성준.Speech is one the most useful interface that enables a person to communicate with distant others while using hands for other tasks. With the growing usage of speech interfaces in mobile devices, home appliances, and automobiles, the research on human-machine speech interface is expanding. This thesis deals with the speech synthesis which enable machines to generate speech. With the application of deep learning technology, the quality of synthesized speech has become similar to that of human speech, but natural style control is still a challenging task. In this thesis, we propose novel techniques for expressing various styles such as prosody and emotion, and for controlling the style of synthesized speech factor-by-factor. First, the conventional style control techniques which have proposed for speech synthesis systems are introduced. In order to control speaker identity, emotion, accent, prosody, we introduce the control method both for statistical parametric-based and deep learning-based speech synthesis systems. We propose a gated recurrent attention (GRA), a novel attention mechanism with a controllable gated recurence. GRA is suitable for learning various styles because it can control the recurrent state for attention corresponds to the location with two gates. By experiments, GRA was found to be more effective in transferring unseen styles, which implies that the GRA outperform in generalization to conventional techniques. We propose a multivariate information minimization method which disentangle three or more latent representations. We show that control factors can be disentangled by minimizing interactive dependency which can be expressed as a sum of mutual information upper bound terms. Since the upper bound estimate converges from the early training stage, there is little performance degradation due to auxiliary loss. The proposed technique is applied to train a text-to-speech synthesizer with multi-lingual, multi-speaker, and multi-style corpora. Subjective listening tests validate the proposed method can improve the synthesizer in terms of quality as well as controllability.음성은 사람이 손으로 다른 일을 하면서도, 멀리 떨어진 상대와 활용할 수 있는 가장 유용한 인터페이스 중 하나이다. 대부분의 사람이 생활에서 밀접하게 접하는 모바일 기기, 가전, 자동차 등에서 음성 인터페이스를 활용하게 되면서, 기계와 사람 간의 음성 인터페이스에 대한 연구가 날로 증가하고 있다. 본 논문은 기계가 음성을 만드는 과정인 음성 합성을 다룬다. 딥 러닝 기술이 적용되면서 합성된 음성의 품질은 사람의 음성과 유사해졌지만, 자연스러운 스타일의 제어는 아직도 도전적인 과제이다. 본 논문에서는 다양한 운율과 감정을 표현할 수 있는 음성을 합성하기 위한 기법들을 제안하며, 스타일을 요소별로 제어하여 손쉽게 원하는 스타일의 음성을 합성할 수 있도록 하는 기법을 제안한다. 먼저 음성 합성을 위해 제안된 기존 스타일 제어 기법들을 소개한다. 화자, 감정, 말투나, 음운 등을 제어하면서도 자연스러운 발화를 합성하고자 통계적 파라미터 음성 합성 시스템을 위해 제안된 기법들과, 딥러닝 기반 음성 합성 시스템을 위해 제안된 기법을 소개한다. 다음으로 두 시퀀스(sequence) 간의 관계를 학습하여, 입력 시퀀스에 따라 출력 시퀀스를 생성하는 어텐션(attention) 기법에 제어 가능한 재귀성을 추가한 게이트 재귀 어텐션(Gated Recurrent Attention) 를 제안한다. 게이트 재귀 어텐션은 일정한 입력에 대해 출력 위치에 따라 달라지는 다양한 출력을 두 개의 게이트를 통해 제어할 수 있어 다양한 스타일을 학습하는데 적합하다. 게이트 재귀 어텐션은 학습 데이터에 없었던 스타일을 학습하고 생성하는데 있어 기존 기법에 비해 자연스러움이나 스타일 유사도 면에서 높은 성능을 보이는 것을 실험을 통해 확인할 수 있었다. 다음으로 세 개 이상의 스타일 요소들의 상호의존성을 제거할 수 있는 기법을 제안한다. 여러개의 제어 요소들(factors)을 변수간 상호의존성 상한 항들의 합으로 나타내고, 이를 최소화하여 의존성을 제거할 수 있음을 보인다. 이 상한 추정치는 학습 초기에 수렴하여 0에 가깝게 유지되기 때문에, 손실함수를 더함으로써 생기는 성능 저하가 거의 없다. 제안하는 기법은 다언어, 다화자, 스타일 데이터베이스로 음성합성기를 학습하는데 활용된다. 15명의 음성 전문가들의 주관적인 듣기 평가를 통해 제안하는 기법이 합성기의 스타일 제어가능성을 높일 뿐만 아니라 합성음의 품질까지 높일 수 있음을 보인다.1 Introduction 1 1.1 Evolution of Speech Synthesis Technology 1 1.2 Attention-based Speech Synthesis Systems 2 1.2.1 Tacotron 2 1.2.2 Deep Convolutional TTS 3 1.3 Non-autoregressive Speech Synthesis Systems 6 1.3.1 Glow-TTS 6 1.3.2 SpeedySpeech 8 1.4 Outline of the thesis 8 2 Style Modeling Techniques for Speech Synthesis 13 2.1 Introduction 13 2.2 Style Modeling Techniques for Statistical Parametric Speech Synthesis 14 2.3 Style Modeling Techniques for Deep Learning-based Speech Synthesis 15 2.4 Summary 17 3 Gated Recurrent Attention for Multi-Style Speech Synthesis 19 3.1 Introduction 19 3.2 Related Works 20 3.2.1 Gated recurrent unit 20 3.2.2 Location-sensitive attention 22 3.3 Gated Recurrent Attention 24 3.4 Experiments and results 28 3.4.1 Tacotron2 with global style tokens 28 3.4.2 Decaying guided attention 29 3.4.3 Datasets and feature processing 30 3.4.4 Evaluation methods 32 3.4.5 Evaluation results 33 3.5 Guided attention and decaying guided attention 34 3.6 Summary 35 4 A Controllable Multi-lingual Multi-speaker Multi-style Text-to-Speech Synthesis with Multivariate Information Minimization 41 4.1 Introduction 41 4.2 Related Works 44 4.2.1 Disentanglement Studies for Speech Synthesis 44 4.2.2 Total Correlation and Mutual Information 45 4.2.3 CLUB:A Contrastive Log-ratio Upper Bound of Mutual Information 46 4.3 Proposed method 46 4.4 Experiments and Results 47 4.4.1 Quality and Naturalness of Speech 51 4.4.2 Speaker and style similarity 52 4.5 Summary 53 5 Conclusions 55 Bibliography 57 초 록 67 감사의 글 69박

Speaker and Expression Factorization for Audiobook Data: Expressiveness and Transplantation

Expressive synthesis from text is a challenging problem. There are two issues. First, read text is often highly expressive to convey the emotion and scenario in the text. Second, since the expressive training speech is not always available for different speakers, it is necessary to develop methods to share the expressive information over speakers. This paper investigates the approach of using very expressive, highly diverse audiobook data from multiple speakers to build an expressive speech synthesis system. Both of two problems are addressed by considering a factorized framework where speaker and emotion are modelled in separate sub-spaces of a cluster adaptive training (CAT) parametric speech synthesis system. The sub-spaces for the expressive state of a speaker and the characteristics of the speaker are jointly trained using a set of audiobooks. In this work, the expressive speech synthesis system works in two distinct modes. In the first mode, the expressive information is given by audio data and the adaptation method is used to extract the expressive information in the audio data. In the second mode, the input of the synthesis system is plain text and a full expressive synthesis system is examined where the expressive state is predicted from the text. In both modes, the expressive information is shared and transplanted over different speakers. Experimental results show that in both modes, the expressive speech synthesis method proposed in this work significantly improves the expressiveness of the synthetic speech for different speakers. Finally, this paper also examines whether it is possible to predict the expressive states from text for multiple speakers using a single model, or whether the prediction process needs to be speaker specific.This is the accepted manuscript. The final version is available from IEEE at http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6995936&filter%3DAND%28p_IS_Number%3A7055953%29

Modelling Speech Dynamics with Trajectory-HMMs

Institute for Communicating and Collaborative SystemsThe conditional independence assumption imposed by the hidden Markov models (HMMs) makes it difficult to model temporal correlation patterns in human speech. Traditionally, this limitation is circumvented by appending the first and second-order regression coefficients to the observation feature vectors. Although this leads to improved performance in recognition tasks, we argue that a straightforward use of dynamic features in HMMs will result in an inferior model, due to the incorrect handling of dynamic constraints. In this thesis I will show that an HMM can be transformed into a Trajectory-HMM capable of generating smoothed output mean trajectories, by performing a per-utterance normalisation. The resulting model can be trained by either maximisingmodel log-likelihood or minimisingmean generation errors on the training data. To combat the exponential growth of paths in searching, the idea of delayed path merging is proposed and a new time-synchronous decoding algorithm built on the concept of token-passing is designed for use in the recognition task. The Trajectory-HMM brings a new way of sharing knowledge between speech recognition and synthesis components, by tackling both problems in a coherent statistical framework. I evaluated the Trajectory-HMM on two different speech tasks using the speaker-dependent MOCHA-TIMIT database. First as a generative model to recover articulatory features from speech signal, where the Trajectory-HMM was used in a complementary way to the conventional HMM modelling techniques, within a joint Acoustic-Articulatory framework. Experiments indicate that the jointly trained acoustic-articulatory models are more accurate (having a lower Root Mean Square error) than the separately trained ones, and that Trajectory-HMM training results in greater accuracy compared with conventional Baum-Welch parameter updating. In addition, the Root Mean Square (RMS) training objective proves to be consistently better than the Maximum Likelihood objective. However, experiment of the phone recognition task shows that the MLE trained Trajectory-HMM, while retaining attractive properties of being a proper generative model, tends to favour over-smoothed trajectories among competing hypothesises, and does not perform better than a conventional HMM. We use this to build an argument that models giving a better fit on training data may suffer a reduction of discrimination by being too faithful to the training data. Finally, experiments on using triphone models show that increasing modelling detail is an effective way to leverage modelling performance with little added complexity in training

IberSPEECH 2020: XI Jornadas en Tecnología del Habla and VII Iberian SLTech

IberSPEECH2020 is a two-day event, bringing together the best researchers and practitioners in speech and language technologies in Iberian languages to promote interaction and discussion. The organizing committee has planned a wide variety of scientific and social activities, including technical paper presentations, keynote lectures, presentation of projects, laboratories activities, recent PhD thesis, discussion panels, a round table, and awards to the best thesis and papers. The program of IberSPEECH2020 includes a total of 32 contributions that will be presented distributed among 5 oral sessions, a PhD session, and a projects session. To ensure the quality of all the contributions, each submitted paper was reviewed by three members of the scientific review committee. All the papers in the conference will be accessible through the International Speech Communication Association (ISCA) Online Archive. Paper selection was based on the scores and comments provided by the scientific review committee, which includes 73 researchers from different institutions (mainly from Spain and Portugal, but also from France, Germany, Brazil, Iran, Greece, Hungary, Czech Republic, Ucrania, Slovenia). Furthermore, it is confirmed to publish an extension of selected papers as a special issue of the Journal of Applied Sciences, “IberSPEECH 2020: Speech and Language Technologies for Iberian Languages”, published by MDPI with fully open access. In addition to regular paper sessions, the IberSPEECH2020 scientific program features the following activities: the ALBAYZIN evaluation challenge session.Red Española de Tecnologías del Habla. Universidad de Valladoli

Proceedings of the 7th Sound and Music Computing Conference

Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010