Exploring efficient neural architectures for linguistic-acoustic mapping in text-to-speech

Conversion from text to speech relies on the accurate mapping from linguistic to acoustic symbol sequences, for which current practice employs recurrent statistical models such as recurrent neural networks. Despite the good performance of such models (in terms of low distortion in the generated speech), their recursive structure with intermediate affine transformations tends to make them slow to train and to sample from. In this work, we explore two different mechanisms that enhance the operational efficiency of recurrent neural networks, and study their performance–speed trade-off. The first mechanism is based on the quasi-recurrent neural network, where expensive affine transformations are removed from temporal connections and placed only on feed-forward computational directions. The second mechanism includes a module based on the transformer decoder network, designed without recurrent connections but emulating them with attention and positioning codes. Our results show that the proposed decoder networks are competitive in terms of distortion when compared to a recurrent baseline, whilst being significantly faster in terms of CPU and GPU inference time. The best performing model is the one based on the quasi-recurrent mechanism, reaching the same level of naturalness as the recurrent neural network based model with a speedup of 11.2 on CPU and 3.3 on GPU.Peer ReviewedPostprint (published version

LSTM Deep Neural Networks Postfiltering for Improving the Quality of Synthetic Voices

Recent developments in speech synthesis have produced systems capable of outcome intelligible speech, but now researchers strive to create models that more accurately mimic human voices. One such development is the incorporation of multiple linguistic styles in various languages and accents. HMM-based Speech Synthesis is of great interest to many researchers, due to its ability to produce sophisticated features with small footprint. Despite such progress, its quality has not yet reached the level of the predominant unit-selection approaches that choose and concatenate recordings of real speech. Recent efforts have been made in the direction of improving these systems. In this paper we present the application of Long-Short Term Memory Deep Neural Networks as a Postfiltering step of HMM-based speech synthesis, in order to obtain closer spectral characteristics to those of natural speech. The results show how HMM-voices could be improved using this approach.Comment: 5 pages, 5 figure

딥러닝을 활용한 스타일 적응형 음성 합성 기법

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2020. 8. 김남수.The neural network-based speech synthesis techniques have been developed over the years. Although neural speech synthesis has shown remarkable generated speech quality, there are still remaining problems such as modeling power in a neural statistical parametric speech synthesis system, style expressiveness, and robust attention model in the end-to-end speech synthesis system. In this thesis, novel alternatives are proposed to resolve these drawbacks of the conventional neural speech synthesis system. In the first approach, we propose an adversarially trained variational recurrent neural network (AdVRNN), which applies a variational recurrent neural network (VRNN) to represent the variability of natural speech for acoustic modeling in neural statistical parametric speech synthesis. Also, we apply an adversarial learning scheme in training AdVRNN to overcome the oversmoothing problem. From the experimental results, we have found that the proposed AdVRNN based method outperforms the conventional RNN-based techniques. In the second approach, we propose a novel style modeling method employing mutual information neural estimator (MINE) in a style-adaptive end-to-end speech synthesis system. MINE is applied to increase target-style information and suppress text information in style embedding by applying MINE loss term in the loss function. The experimental results show that the MINE-based method has shown promising performance in both speech quality and style similarity for the global style token-Tacotron. In the third approach, we propose a novel attention method called memory attention for end-to-end speech synthesis, which is inspired by the gating mechanism of long-short term memory (LSTM). Leveraging the gating technique's sequence modeling power in LSTM, memory attention obtains the stable alignment from the content-based and location-based features. We evaluate the memory attention and compare its performance with various conventional attention techniques in single speaker and emotional speech synthesis scenarios. From the results, we conclude that memory attention can generate speech with large variability robustly. In the last approach, we propose selective multi-attention for style-adaptive end-to-end speech synthesis systems. The conventional single attention model may limit the expressivity representing numerous alignment paths depending on style. To achieve a variation in attention alignment, we propose using a multi-attention model with a selection network. The multi-attention plays a role in generating candidates for the target style, and the selection network choose the most proper attention among the multi-attention. The experimental results show that selective multi-attention outperforms the conventional single attention techniques in multi-speaker speech synthesis and emotional speech synthesis.딥러닝 기반의 음성 합성 기술은 지난 몇 년간 횔발하게 개발되고 있다. 딥러닝의 다양한 기법을 사용하여 음성 합성 품질은 비약적으로 발전했지만, 아직 딥러닝 기반의 음성 합성에는 여러 문제가 존재한다. 딥러닝 기반의 통계적 파라미터 기법의 경우 음향 모델의 deterministic한 모델을 활용하여 모델링 능력의 한계가 있으며, 종단형 모델의 경우 스타일을 표현하는 능력과 강인한 어텐션(attention)에 대한 이슈가 끊임없이 재기되고 있다. 본 논문에서는 이러한 기존의 딥러닝 기반 음성 합성 시스템의 단점을 해결할 새로운 대안을 제안한다. 첫 번째 접근법으로서, 뉴럴 통계적 파라미터 방식의 음향 모델링을 고도화하기 위한 adversarially trained variational recurrent neural network (AdVRNN) 기법을 제안한다. AdVRNN 기법은 VRNN을 음성 합성에 적용하여 음성의 변화를 stochastic 하고 자세하게 모델링할 수 있도록 하였다. 또한, 적대적 학습적(adversarial learning) 기법을 활용하여 oversmoothing 문제를 최소화 시키도록 하였다. 이러한 제안된 알고리즘은 기존의 순환 신경망 기반의 음향 모델과 비교하여 성능이 향상됨을 확인하였다. 두 번째 접근법으로서, 스타일 적응형 종단형 음성 합성 기법을 위한 상호 정보량 기반의 새로운 학습 기법을 제안한다. 기존의 global style token(GST) 기반의 스타일 음성 합성 기법의 경우, 비지도 학습을 사용하므로 원하는 목표 스타일이 있어도 이를 중점적으로 학습시키기 어려웠다. 이를 해결하기 위해 GST의 출력과 목표 스타일 임베딩 벡터의 상호 정보량을 최대화 하도록 학습 시키는 기법을 제안하였다. 상호 정보량을 종단형 모델의 손실함수에 적용하기 위해서 mutual information neural estimator(MINE) 기법을 도입하였고 다화자 모델을 통해 기존의 GST 기법에 비해 목표 스타일을 보다 중점적으로 학습시킬 수 있음을 확인하였다. 세번째 접근법으로서, 강인한 종단형 음성 합성의 어텐션인 memory attention을 제안한다. Long-short term memory(LSTM)의 gating 기술은 sequence를 모델링하는데 높은 성능을 보여왔다. 이러한 기술을 어텐션에 적용하여 다양한 스타일을 가진 음성에서도 어텐션의 끊김, 반복 등을 최소화할 수 있는 기법을 제안한다. 단일 화자와 감정 음성 합성 기법을 토대로 memory attention의 성능을 확인하였으며 기존 기법 대비 보다 안정적인 어텐션 곡선을 얻을 수 있음을 확인하였다. 마지막 접근법으로서, selective multi-attention (SMA)을 활용한 스타일 적응형 종단형 음성 합성 어텐션 기법을 제안한다. 기존의 스타일 적응형 종단형 음성 합성의 연구에서는 낭독체 단일화자의 경우와 같은 단일 어텐션을 사용하여 왔다. 하지만 스타일 음성의 경우 보다 다양한 어텐션 표현을 요구한다. 이를 위해 다중 어텐션을 활용하여 후보들을 생성하고 이를 선택 네트워크를 활용하여 최적의 어텐션을 선택하는 기법을 제안한다. SMA 기법은 기존의 어텐션과의 비교 실험을 통하여 보다 많은 스타일을 안정적으로 표현할 수 있음을 확인하였다.1 Introduction 1 1.1 Background 1 1.2 Scope of thesis 3 2 Neural Speech Synthesis System 7 2.1 Overview of a Neural Statistical Parametric Speech Synthesis System 7 2.2 Overview of End-to-end Speech Synthesis System 9 2.3 Tacotron2 10 2.4 Attention Mechanism 12 2.4.1 Location Sensitive Attention 12 2.4.2 Forward Attention 13 2.4.3 Dynamic Convolution Attention 14 3 Neural Statistical Parametric Speech Synthesis using AdVRNN 17 3.1 Introduction 17 3.2 Background 19 3.2.1 Variational Autoencoder 19 3.2.2 Variational Recurrent Neural Network 20 3.3 Speech Synthesis Using AdVRNN 22 3.3.1 AdVRNN based Acoustic Modeling 23 3.3.2 Training Procedure 24 3.4 Experiments 25 3.4.1 Objective performance evaluation 28 3.4.2 Subjective performance evaluation 29 3.5 Summary 29 4 Speech Style Modeling Method using Mutual Information for End-to-End Speech Synthesis 31 4.1 Introduction 31 4.2 Background 33 4.2.1 Mutual Information 33 4.2.2 Mutual Information Neural Estimator 34 4.2.3 Global Style Token 34 4.3 Style Token end-to-end speech synthesis using MINE 35 4.4 Experiments 36 4.5 Summary 38 5 Memory Attention: Robust Alignment using Gating Mechanism for End-to-End Speech Synthesis 45 5.1 Introduction 45 5.2 BACKGROUND 48 5.3 Memory Attention 49 5.4 Experiments 52 5.4.1 Experiments on Single Speaker Speech Synthesis 53 5.4.2 Experiments on Emotional Speech Synthesis 56 5.5 Summary 59 6 Selective Multi-attention for style-adaptive end-to-End Speech Syn-thesis 63 6.1 Introduction 63 6.2 BACKGROUND 65 6.3 Selective multi-attention model 66 6.4 EXPERIMENTS 67 6.4.1 Multi-speaker speech synthesis experiments 68 6.4.2 Experiments on Emotional Speech Synthesis 73 6.5 Summary 77 7 Conclusions 79 Bibliography 83 요약 93 감사의 글 95Docto

Tacotron: Towards End-to-End Speech Synthesis

A text-to-speech synthesis system typically consists of multiple stages, such as a text analysis frontend, an acoustic model and an audio synthesis module. Building these components often requires extensive domain expertise and may contain brittle design choices. In this paper, we present Tacotron, an end-to-end generative text-to-speech model that synthesizes speech directly from characters. Given pairs, the model can be trained completely from scratch with random initialization. We present several key techniques to make the sequence-to-sequence framework perform well for this challenging task. Tacotron achieves a 3.82 subjective 5-scale mean opinion score on US English, outperforming a production parametric system in terms of naturalness. In addition, since Tacotron generates speech at the frame level, it's substantially faster than sample-level autoregressive methods.Comment: Submitted to Interspeech 2017. v2 changed paper title to be consistent with our conference submission (no content change other than typo fixes