Toward an interpretive framework of two-dimensional speech-signal processing

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 177-179).Traditional representations of speech are derived from short-time segments of the signal and result in time-frequency distributions of energy such as the short-time Fourier transform and spectrogram. Speech-signal models of such representations have had utility in a variety of applications such as speech analysis, recognition, and synthesis. Nonetheless, they do not capture spectral, temporal, and joint spectrotemporal energy fluctuations (or "modulations") present in local time-frequency regions of the time-frequency distribution. Inspired by principles from image processing and evidence from auditory neurophysiological models, a variety of twodimensional (2-D) processing techniques have been explored in the literature as alternative representations of speech; however, speech-based models are lacking in this framework. This thesis develops speech-signal models for a particular 2-D processing approach in which 2-D Fourier transforms are computed on local time-frequency regions of the canonical narrowband or wideband spectrogram; we refer to the resulting transformed space as the Grating Compression Transform (GCT). We argue for a 2-D sinusoidal-series amplitude modulation model of speech content in the spectrogram domain that relates to speech production characteristics such as pitch/noise of the source, pitch dynamics, formant structure and dynamics, and offset/onset content. Narrowband- and wideband-based models are shown to exhibit important distinctions in interpretation and oftentimes "dual" behavior. In the transformed GCT space, the modeling results in a novel taxonomy of signal behavior based on the distribution of formant and onset/offset content in the transformed space via source characteristics. Our formulation provides a speech-specific interpretation of the concept of "modulation" in 2-D processing in contrast to existing approaches that have done so either phenomenologically through qualitative analyses and/or implicitly through data-driven machine learning approaches. One implication of the proposed taxonomy is its potential for interpreting transformations of other time-frequency distributions such as the auditory spectrogram which is generally viewed as being "narrowband"/"wideband" in its low/high-frequency regions. The proposed signal model is evaluated in several ways. First, we perform analysis of synthetic speech signals to characterize its properties and limitations. Next, we develop an algorithm for analysis/synthesis of spectrograms using the model and demonstrate its ability to accurately represent real speech content. As an example application, we further apply the models in cochannel speaker separation, exploiting the GCT's ability to distribute speaker-specific content and often recover overlapping information through demodulation and interpolation in the 2-D GCT space. Specifically, in multi-pitch estimation, we demonstrate the GCT's ability to accurately estimate separate and crossing pitch tracks under certain conditions. Finally, we demonstrate the model's ability to separate mixtures of speech signals using both prior and estimated pitch information. Generalization to other speech-signal processing applications is proposed.by Tianyu Tom Wang.Ph.D

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

A Parametric Sound Object Model for Sound Texture Synthesis

This thesis deals with the analysis and synthesis of sound textures based on parametric sound objects. An overview is provided about the acoustic and perceptual principles of textural acoustic scenes, and technical challenges for analysis and synthesis are considered. Four essential processing steps for sound texture analysis are identifi ed, and existing sound texture systems are reviewed, using the four-step model as a guideline. A theoretical framework for analysis and synthesis is proposed. A parametric sound object synthesis (PSOS) model is introduced, which is able to describe individual recorded sounds through a fi xed set of parameters. The model, which applies to harmonic and noisy sounds, is an extension of spectral modeling and uses spline curves to approximate spectral envelopes, as well as the evolution of parameters over time. In contrast to standard spectral modeling techniques, this representation uses the concept of objects instead of concatenated frames, and it provides a direct mapping between sounds of diff erent length. Methods for automatic and manual conversion are shown. An evaluation is presented in which the ability of the model to encode a wide range of di fferent sounds has been examined. Although there are aspects of sounds that the model cannot accurately capture, such as polyphony and certain types of fast modulation, the results indicate that high quality synthesis can be achieved for many different acoustic phenomena, including instruments and animal vocalizations. In contrast to many other forms of sound encoding, the parametric model facilitates various techniques of machine learning and intelligent processing, including sound clustering and principal component analysis. Strengths and weaknesses of the proposed method are reviewed, and possibilities for future development are discussed

Leveraging audio-visual speech effectively via deep learning

The rising popularity of neural networks, combined with the recent proliferation of online audio-visual media, has led to a revolution in the way machines encode, recognize, and generate acoustic and visual speech. Despite the ubiquity of naturally paired audio-visual data, only a limited number of works have applied recent advances in deep learning to leverage the duality between audio and video within this domain. This thesis considers the use of neural networks to learn from large unlabelled datasets of audio-visual speech to enable new practical applications. We begin by training a visual speech encoder that predicts latent features extracted from the corresponding audio on a large unlabelled audio-visual corpus. We apply the trained visual encoder to improve performance on lip reading in real-world scenarios. Following this, we extend the idea of video learning from audio by training a model to synthesize raw speech directly from raw video, without the need for text transcriptions. Remarkably, we find that this framework is capable of reconstructing intelligible audio from videos of new, previously unseen speakers. We also experiment with a separate speech reconstruction framework, which leverages recent advances in sequence modeling and spectrogram inversion to improve the realism of the generated speech. We then apply our research in video-to-speech synthesis to advance the state-of-the-art in audio-visual speech enhancement, by proposing a new vocoder-based model that performs particularly well under extremely noisy scenarios. Lastly, we aim to fully realize the potential of paired audio-visual data by proposing two novel frameworks that leverage acoustic and visual speech to train two encoders that learn from each other simultaneously. We leverage these pre-trained encoders for deepfake detection, speech recognition, and lip reading, and find that they consistently yield improvements over training from scratch.Open Acces