Adding expressiveness to unit selection speech synthesis and to numerical voice production

La parla és una de les formes de comunicació més naturals i directes entre éssers humans, ja que codifica un missatge i també claus paralingüístiques sobre l’estat emocional del locutor, el to o la seva intenció, esdevenint així fonamental en la consecució d’una interacció humà-màquina (HCI) més natural. En aquest context, la generació de parla expressiva pel canal de sortida d’HCI és un element clau en el desenvolupament de tecnologies assistencials o assistents personals entre altres aplicacions. La parla sintètica pot ser generada a partir de parla enregistrada utilitzant mètodes basats en corpus com la selecció d’unitats (US), que poden aconseguir resultats d’alta qualitat però d’expressivitat restringida a la pròpia del corpus. A fi de millorar la qualitat de la sortida de la síntesi, la tendència actual és construir bases de dades de veu cada cop més grans, seguint especialment l’aproximació de síntesi anomenada End-to-End basada en tècniques d’aprenentatge profund. Tanmateix, enregistrar corpus ad-hoc per cada estil expressiu desitjat pot ser extremadament costós o fins i tot inviable si el locutor no és capaç de realitzar adequadament els estils requerits per a una aplicació donada (ex: cant en el domini de la narració de contes). Alternativament, nous mètodes basats en la física de la producció de veu s’han desenvolupat a la darrera dècada gràcies a l’increment en la potència computacional. Per exemple, vocals o diftongs poden ser obtinguts utilitzant el mètode d’elements finits (FEM) per simular la propagació d’ones acústiques a través d’una geometria 3D realista del tracte vocal obtinguda a partir de ressonàncies magnètiques (MRI). Tanmateix, atès que els principals esforços en aquests mètodes de producció numèrica de veu s’han focalitzat en la millora del modelat del procés de generació de veu, fins ara s’ha prestat poca atenció a la seva expressivitat. A més, la col·lecció de dades per aquestes simulacions és molt costosa, a més de requerir un llarg postprocessament manual com el necessari per extreure geometries 3D del tracte vocal a partir de MRI. L’objectiu de la tesi és afegir expressivitat en un sistema que genera veu neutra, sense haver d’adquirir dades expressives del locutor original. Per un costat, s’afegeixen capacitats expressives a un sistema de conversió de text a parla basat en selecció d’unitats (US-TTS) dotat d’un corpus de veu neutra, per adreçar necessitats específiques i concretes en l’àmbit de la narració de contes, com són la veu cantada o situacions de suspens. A tal efecte, la veu és parametritzada utilitzant un model harmònic i transformada a l’estil expressiu desitjat d’acord amb un sistema expert. Es presenta una primera aproximació, centrada en la síntesi de suspens creixent per a la narració de contes, i es demostra la seva viabilitat pel que fa a naturalitat i qualitat de narració de contes. També s’afegeixen capacitats de cant al sistema US-TTS mitjançant la integració de mòduls de transformació de parla a veu cantada en el pipeline del TTS, i la incorporació d’un mòdul de generació de prosòdia expressiva que permet al mòdul de US seleccionar unitats més properes a la prosòdia cantada obtinguda a partir de la partitura d’entrada. Això resulta en un framework de síntesi de conversió de text a parla i veu cantada basat en selecció d’unitats (US-TTS&S) que pot generar veu parlada i cantada a partir d'un petit corpus de veu neutra (~2.6h). D’acord amb els resultats objectius, l’estratègia de US guiada per la partitura permet reduir els factors de modificació de pitch requerits per produir veu cantada a partir de les unitats de veu parlada seleccionades, però en canvi té una efectivitat limitada amb els factors de modificació de les durades degut a la curta durada de les vocals parlades neutres. Els resultats dels tests perceptius mostren que tot i òbviament obtenir una naturalitat inferior a la oferta per un sintetitzador professional de veu cantada, el framework pot adreçar necessitats puntuals de veu cantada per a la síntesis de narració de contes amb una qualitat raonable. La incorporació d’expressivitat s’investiga també en la simulació numèrica 3D de vocals basada en FEM mitjançant modificacions de les senyals d’excitació glotal utilitzant una aproximació font-filtre de producció de veu. Aquestes senyals es generen utilitzant un model Liljencrants-Fant (LF) controlat amb el paràmetre de forma del pols Rd, que permet explorar el continu de fonació lax-tens a més del rang de freqüències fonamentals, F0, de la veu parlada. S’analitza la contribució de la font glotal als modes d’alt ordre en la síntesis FEM de les vocals cardinals [a], [i] i [u] mitjançant la comparació dels valors d’energia d’alta freqüència (HFE) obtinguts amb geometries realistes i simplificades del tracte vocal. Les simulacions indiquen que els modes d’alt ordre es preveuen perceptivament rellevants d’acord amb valors de referència de la literatura, particularment per a fonacions tenses i/o F0s altes. En canvi, per a vocals amb una fonació laxa i/o F0s baixes els nivells d’HFE poden resultar inaudibles, especialment si no hi ha soroll d’aspiració en la font glotal. Després d’aquest estudi preliminar, s’han analitzat les característiques d’excitació de vocals alegres i agressives d’un corpus paral·lel de veu en castellà amb l’objectiu d’incorporar aquests estils expressius de veu tensa en la simulació numèrica de veu. Per a tal efecte, s’ha usat el vocoder GlottDNN per analitzar variacions d’F0 i pendent espectral relacionades amb l’excitació glotal en vocals [a]. Aquestes variacions es mapegen mitjançant la comparació amb vocals sintètiques en valors d’F0 i Rd per simular vocals que s’assemblin als estils alegre i agressiu. Els resultats mostren que és necessari incrementar l’F0 i disminuir l’Rd respecte la veu neutra, amb variacions majors per a alegre que per agressiu, especialment per a vocals accentuades. Els resultats aconseguits en les investigacions realitzades validen la possibilitat d’afegir expressivitat a la síntesi basada en corpus US-TTS i a la simulació numèrica de veu basada en FEM. Tanmateix, encara hi ha marge de millora. Per exemple, l’estratègia aplicada a la producció numèrica de veu es podria millorar estudiant i desenvolupant mètodes de filtratge invers així com incorporant modificacions del tracte vocal, mentre que el framework US-TTS&S es podria beneficiar dels avenços en tècniques de transformació de veu incloent transformacions de la qualitat de veu, aprofitant l’experiència adquirida en la simulació numèrica de vocals expressives.El habla es una de las formas de comunicación más naturales y directas entre seres humanos, ya que codifica un mensaje y también claves paralingüísticas sobre el estado emocional del locutor, el tono o su intención, convirtiéndose así en fundamental en la consecución de una interacción humano-máquina (HCI) más natural. En este contexto, la generación de habla expresiva para el canal de salida de HCI es un elemento clave en el desarrollo de tecnologías asistenciales o asistentes personales entre otras aplicaciones. El habla sintética puede ser generada a partir de habla gravada utilizando métodos basados en corpus como la selección de unidades (US), que pueden conseguir resultados de alta calidad, pero de expresividad restringida a la propia del corpus. A fin de mejorar la calidad de la salida de la síntesis, la tendencia actual es construir bases de datos de voz cada vez más grandes, siguiendo especialmente la aproximación de síntesis llamada End-to-End basada en técnicas de aprendizaje profundo. Sin embargo, gravar corpus ad-hoc para cada estilo expresivo deseado puede ser extremadamente costoso o incluso inviable si el locutor no es capaz de realizar adecuadamente los estilos requeridos para una aplicación dada (ej: canto en el dominio de la narración de cuentos). Alternativamente, nuevos métodos basados en la física de la producción de voz se han desarrollado en la última década gracias al incremento en la potencia computacional. Por ejemplo, vocales o diptongos pueden ser obtenidos utilizando el método de elementos finitos (FEM) para simular la propagación de ondas acústicas a través de una geometría 3D realista del tracto vocal obtenida a partir de resonancias magnéticas (MRI). Sin embargo, dado que los principales esfuerzos en estos métodos de producción numérica de voz se han focalizado en la mejora del modelado del proceso de generación de voz, hasta ahora se ha prestado poca atención a su expresividad. Además, la colección de datos para estas simulaciones es muy costosa, además de requerir un largo postproceso manual como el necesario para extraer geometrías 3D del tracto vocal a partir de MRI. El objetivo de la tesis es añadir expresividad en un sistema que genera voz neutra, sin tener que adquirir datos expresivos del locutor original. Per un lado, se añaden capacidades expresivas a un sistema de conversión de texto a habla basado en selección de unidades (US-TTS) dotado de un corpus de voz neutra, para abordar necesidades específicas y concretas en el ámbito de la narración de cuentos, como son la voz cantada o situaciones de suspense. Para ello, la voz se parametriza utilizando un modelo harmónico y se transforma al estilo expresivo deseado de acuerdo con un sistema experto. Se presenta una primera aproximación, centrada en la síntesis de suspense creciente para la narración de cuentos, y se demuestra su viabilidad en cuanto a naturalidad y calidad de narración de cuentos. También se añaden capacidades de canto al sistema US-TTS mediante la integración de módulos de transformación de habla a voz cantada en el pipeline del TTS, y la incorporación de un módulo de generación de prosodia expresiva que permite al módulo de US seleccionar unidades más cercanas a la prosodia cantada obtenida a partir de la partitura de entrada. Esto resulta en un framework de síntesis de conversión de texto a habla y voz cantada basado en selección de unidades (US-TTS&S) que puede generar voz hablada y cantada a partir del mismo pequeño corpus de voz neutra (~2.6h). De acuerdo con los resultados objetivos, la estrategia de US guiada por la partitura permite reducir los factores de modificación de pitch requeridos para producir voz cantada a partir de las unidades de voz hablada seleccionadas, pero en cambio tiene una efectividad limitada con los factores de modificación de duraciones debido a la corta duración de las vocales habladas neutras. Los resultados de las pruebas perceptivas muestran que, a pesar de obtener una naturalidad obviamente inferior a la ofrecida por un sintetizador profesional de voz cantada, el framework puede abordar necesidades puntuales de voz cantada para la síntesis de narración de cuentos con una calidad razonable. La incorporación de expresividad se investiga también en la simulación numérica 3D de vocales basada en FEM mediante modificaciones en las señales de excitación glotal utilizando una aproximación fuente-filtro de producción de voz. Estas señales se generan utilizando un modelo Liljencrants-Fant (LF) controlado con el parámetro de forma del pulso Rd, que permite explorar el continuo de fonación laxo-tenso además del rango de frecuencias fundamentales, F0, de la voz hablada. Se analiza la contribución de la fuente glotal a los modos de alto orden en la síntesis FEM de las vocales cardinales [a], [i] y [u] mediante la comparación de los valores de energía de alta frecuencia (HFE) obtenidos con geometrías realistas y simplificadas del tracto vocal. Las simulaciones indican que los modos de alto orden se prevén perceptivamente relevantes de acuerdo con valores de referencia de la literatura, particularmente para fonaciones tensas y/o F0s altas. En cambio, para vocales con una fonación laxa y/o F0s bajas los niveles de HFE pueden resultar inaudibles, especialmente si no hay ruido de aspiración en la fuente glotal. Después de este estudio preliminar, se han analizado las características de excitación de vocales alegres y agresivas de un corpus paralelo de voz en castellano con el objetivo de incorporar estos estilos expresivos de voz tensa en la simulación numérica de voz. Para ello, se ha usado el vocoder GlottDNN para analizar variaciones de F0 y pendiente espectral relacionadas con la excitación glotal en vocales [a]. Estas variaciones se mapean mediante la comparación con vocales sintéticas en valores de F0 y Rd para simular vocales que se asemejen a los estilos alegre y agresivo. Los resultados muestran que es necesario incrementar la F0 y disminuir la Rd respecto la voz neutra, con variaciones mayores para alegre que para agresivo, especialmente para vocales acentuadas. Los resultados conseguidos en las investigaciones realizadas validan la posibilidad de añadir expresividad a la síntesis basada en corpus US-TTS y a la simulación numérica de voz basada en FEM. Sin embargo, hay margen de mejora. Por ejemplo, la estrategia aplicada a la producción numérica de voz se podría mejorar estudiando y desarrollando métodos de filtrado inverso, así como incorporando modificaciones del tracto vocal, mientras que el framework US-TTS&S desarrollado se podría beneficiar de los avances en técnicas de transformación de voz incluyendo transformaciones de la calidad de la voz, aprovechando la experiencia adquirida en la simulación numérica de vocales expresivas.Speech is one of the most natural and direct forms of communication between human beings, as it codifies both a message and paralinguistic cues about the emotional state of the speaker, its mood, or its intention, thus becoming instrumental in pursuing a more natural Human Computer Interaction (HCI). In this context, the generation of expressive speech for the HCI output channel is a key element in the development of assistive technologies or personal assistants among other applications. Synthetic speech can be generated from recorded speech using corpus-based methods such as Unit-Selection (US), which can achieve high quality results but whose expressiveness is restricted to that available in the speech corpus. In order to improve the quality of the synthesis output, the current trend is to build ever larger speech databases, especially following the so-called End-to-End synthesis approach based on deep learning techniques. However, recording ad-hoc corpora for each and every desired expressive style can be extremely costly, or even unfeasible if the speaker is unable to properly perform the styles required for a given application (e.g., singing in the storytelling domain). Alternatively, new methods based on the physics of voice production have been developed in the last decade thanks to the increase in computing power. For instance, vowels or diphthongs can be obtained using the Finite Element Method (FEM) to simulate the propagation of acoustic waves through a 3D realistic vocal tract geometry obtained from Magnetic Resonance Imaging (MRI). However, since the main efforts in these numerical voice production methods have been focused on improving the modelling of the voice generation process, little attention has been paid to its expressiveness up to now. Furthermore, the collection of data for such simulations is very costly, besides requiring manual time-consuming postprocessing like that needed to extract 3D vocal tract geometries from MRI. The aim of the thesis is to add expressiveness into a system that generates neutral voice, without having to acquire expressive data from the original speaker. One the one hand, expressive capabilities are added to a Unit-Selection Text-to-Speech (US-TTS) system fed with a neutral speech corpus, to address specific and timely needs in the storytelling domain, such as for singing or in suspenseful situations. To this end, speech is parameterised using a harmonic-based model and subsequently transformed to the target expressive style according to an expert system. A first approach dealing with the synthesis of storytelling increasing suspense shows the viability of the proposal in terms of naturalness and storytelling quality. Singing capabilities are also added to the US-TTS system through the integration of Speech-to-Singing (STS) transformation modules into the TTS pipeline, and by incorporating an expressive prosody generation module that allows the US to select units closer to the target singing prosody obtained from the input score. This results in a Unit Selection based Text-to-Speech-and-Singing (US-TTS&S) synthesis framework that can generate both speech and singing from the same neutral speech small corpus (~2.6 h). According to the objective results, the score-driven US strategy can reduce the pitch scaling factors required to produce singing from the selected spoken units, but its effectiveness is limited regarding the time-scale requirements due to the short duration of the spoken vowels. Results from the perceptual tests show that although the obtained naturalness is obviously far from that given by a professional singing synthesiser, the framework can address eventual singing needs for synthetic storytelling with a reasonable quality. The incorporation of expressiveness is also investigated in the 3D FEM-based numerical simulation of vowels through modifications of the glottal flow signals following a source-filter approach of voice production. These signals are generated using a Liljencrants-Fant (LF) model controlled with the glottal shape parameter Rd, which allows exploring the tense-lax continuum of phonation besides the spoken vocal range of fundamental frequency values, F0. The contribution of the glottal source to higher order modes in the FEM synthesis of cardinal vowels [a], [i] and [u] is analysed through the comparison of the High Frequency Energy (HFE) values obtained with realistic and simplified 3D geometries of the vocal tract. The simulations indicate that higher order modes are expected to be perceptually relevant according to reference values stated in the literature, particularly for tense phonations and/or high F0s. Conversely, vowels with a lax phonation and/or low F0s can result in inaudible HFE levels, especially if aspiration noise is not present in the glottal source. After this preliminary study, the excitation characteristics of happy and aggressive vowels from a Spanish parallel speech corpus are analysed with the aim of incorporating this tense voice expressive styles into the numerical production of voice. To that effect, the GlottDNN vocoder is used to analyse F0 and spectral tilt variations associated with the glottal excitation on vowels [a]. These variations are mapped through the comparison with synthetic vowels into F0 and Rd values to simulate vowels resembling happy and aggressive styles. Results show that it is necessary to increase F0 and decrease Rd with respect to neutral speech, with larger variations for happy than aggressive style, especially for the stressed [a] vowels. The results achieved in the conducted investigations validate the possibility of adding expressiveness to both corpus-based US-TTS synthesis and FEM-based numerical simulation of voice. Nevertheless, there is still room for improvement. For instance, the strategy applied to the numerical voice production could be improved by studying and developing inverse filtering approaches as well as incorporating modifications of the vocal tract, whereas the developed US-TTS&S framework could benefit from advances in voice transformation techniques including voice quality modifications, taking advantage of the experience gained in the numerical simulation of expressive vowels

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

Articulatory-based Speech Processing Methods for Foreign Accent Conversion

The objective of this dissertation is to develop speech processing methods that enable without altering their identity. We envision accent conversion primarily as a tool for pronunciation training, allowing non-native speakers to hear their native-accented selves. With this application in mind, we present two methods of accent conversion. The first assumes that the voice quality/identity of speech resides in the glottal excitation, while the linguistic content is contained in the vocal tract transfer function. Accent conversion is achieved by convolving the glottal excitation of a non-native speaker with the vocal tract transfer function of a native speaker. The result is perceived as 60 percent less accented, but it is no longer identified as the same individual. The second method of accent conversion selects segments of speech from a corpus of non-native speech based on their acoustic or articulatory similarity to segments from a native speaker. We predict that articulatory features provide a more speaker-independent representation of speech and are therefore better gauges of linguistic similarity across speakers. To test this hypothesis, we collected a custom database containing simultaneous recordings of speech and the positions of important articulators (e.g. lips, jaw, tongue) for a native and non-native speaker. Resequencing speech from a non-native speaker based on articulatory similarity with a native speaker achieved a 20 percent reduction in accent. The approach is particularly appealing for applications in pronunciation training because it modifies speech in a way that produces realistically achievable changes in accent (i.e., since the technique uses sounds already produced by the non-native speaker). A second contribution of this dissertation is the development of subjective and objective measures to assess the performance of accent conversion systems. This is a difficult problem because, in most cases, no ground truth exists. Subjective evaluation is further complicated by the interconnected relationship between accent and identity, but modifications of the stimuli (i.e. reverse speech and voice disguises) allow the two components to be separated. Algorithms to measure objectively accent, quality, and identity are shown to correlate well with their subjective counterparts

LSTM based voice conversion for laryngectomees

This paper describes a voice conversion system designed withthe aim of improving the intelligibility and pleasantness of oe-sophageal voices. Two different systems have been built, oneto transform the spectral magnitude and another one for thefundamental frequency, both based on DNNs. Ahocoder hasbeen used to extract the spectral information (mel cepstral co-efficients) and a specific pitch extractor has been developed tocalculate the fundamental frequency of the oesophageal voices.The cepstral coefficients are converted by means of an LSTMnetwork. The conversion of the intonation curve is implementedthrough two different LSTM networks, one dedicated to thevoiced unvoiced detection and another one for the predictionof F0 from the converted cepstral coefficients. The experi-ments described here involve conversion from one oesophagealspeaker to a specific healthy voice. The intelligibility of thesignals has been measured with a Kaldi based ASR system. Apreference test has been implemented to evaluate the subjectivepreference of the obtained converted voices comparing themwith the original oesophageal voice. The results show that spec-tral conversion improves ASR while restoring the intonation ispreferred by human listenersThis work has been partially funded by the Spanish Ministryof Economy and Competitiveness with FEDER support (RE-STORE project, TEC2015-67163-C2-1-R), the Basque Govern-ment (BerbaOla project, KK-2018/00014) and from the Euro-pean Unions H2020 research and innovation programme un-der the Marie Curie European Training Network ENRICH(675324)

Voice Quality Modelling for Expressive Speech Synthesis

This paper presents the perceptual experiments that were carried out in order to validate the methodology of transforming expressive speech styles using voice quality (VoQ) parameters modelling, along with the well-known prosody (F0, duration, and energy), from a neutral style into a number of expressive ones. The main goal was to validate the usefulness of VoQ in the enhancement of expressive synthetic speech in terms of speech quality and style identification. A harmonic plus noise model (HNM) was used to modify VoQ and prosodic parameters that were extracted from an expressive speech corpus. Perception test results indicated the improvement of obtained expressive speech styles using VoQ modelling along with prosodic characteristics

Voice Quality Modelling for Expressive Speech Synthesis

This paper presents the perceptual experiments that were carried out in order to validate the methodology of transforming expressive speech styles using voice quality (VoQ) parameters modelling, along with the well-known prosody ( 0 , duration, and energy), from a neutral style into a number of expressive ones. The main goal was to validate the usefulness of VoQ in the enhancement of expressive synthetic speech in terms of speech quality and style identification. A harmonic plus noise model (HNM) was used to modify VoQ and prosodic parameters that were extracted from an expressive speech corpus. Perception test results indicated the improvement of obtained expressive speech styles using VoQ modelling along with prosodic characteristics

Speech synthesis based on a harmonic model

The wide range of potential commercial applications for a com puter system capable of automatically converting text to speech (TTS) has stimulated decades of research. One of the currently most successful approaches to synthesising speech, concatenative TTS synthesis, combines prerecorded speech units to build full utterances. However, th e prosody of the stored units is often not consistent with that of the target utterance and m ust be altered. Furthermore, several types of mismatch can occur at unit boundaries and must be smoothed. Thus, pitch and time-scale modification techniques as well as smoothing algorithms play a critical role in all concatenative-based systems. This thesis presents the developm ent of a concatenative TTS system based on a harm onic model and incorporating new pitch and time-scaling as well as smoothing algorithms. Experim ent has shown our system capable of both very high quality prosodic modification and synthesis. Results com pare very favourably with those of existing state-of-the-art systems

Prosody Modification using Allpass Residual of Speech Signals

In this paper, we attempt to signify the role of phase spectrum of speech signals in acquiring an accurate estimate of excitation source for prosody modification. The phase spectrum is parametrically modeled as the response of an all pass (AP) filter, and the filter coefficients are estimated by considering the linear prediction (LP) residual as the output of the AP filter. The resultant residual signal, namely AP residual, exhibits unambiguous peaks corresponding to epochs, which are chosen as pitch markers for prosody modification. This strategy efficiently removes ambiguities associated with pitch marking, required for pitch synchronous overlap-add (PSOLA) method. The prosody modification using AP residual is advantageous than time domain PSOLA (TD-PSOLA) using speech signals, as it offers fewer distortions due to its flat magnitude spectrum. Windowing centered around unambiguous peaks in AP residual is used for segmentation, followed by pitch/duration modification of AP residual by mapping of pitch markers. The modified speech signal is obtained from modified AP residual using synthesis filters. The mean opinion scores are used for performance evaluation of the proposed method, and it is observed that the AP residual-based method delivers equivalent performance as that of LP residual based method using epochs, and better performance than the linear prediction PSOLA (LP-PSOLA)

자동 운율 복제를 위한 모음 길이와 기본 주파수 예측

학위논문 (석사)-- 서울대학교 대학원 : 인문대학 협동과정 인지과학전공, 2018. 8. 정민화.The use of computers to help people improve their pronunciation skills of a foreign language has rapidly increased in the last decades. Majority of such Computer-Assisted Pronunciation Training (CAPT) systems have been focused on teaching correct pronunciation of segments only, however, while prosody received much less attention. One of the new approaches to prosody training is self-imitation learning. Prosodic features from a native utterance are transplanted onto learners own speech, and given back as corrective feedback. The main drawback is that this technique requires two identical sets of native and non-native utterances, which makes its actual implementation cumbersome and inflexible. As a preliminary research towards developing a new method of prosody transplantation, the first part of the study surveys previous related works and points out their advantages and drawbacks. We also compare prosodic systems of Korean and English, point out major areas of mistakes that Korean learners of English tend to do, and then we analyze acoustic features that this mistakes are correlated with. We suggest that transplantation of vowel duration and fundamental frequency will be the most effective for self-imitation learning by Korean speakers of English. The second part of this study introduces a new proposed model for prosody transplantation. Instead of transplanting acoustic values from a pre-recorded utterance, we suggest to use a deep neural network (DNN) based system to predict them instead. Three different models are built and described: baseline recurrent neural network (RNN), long short-term memory (LSTM) model and gated recurrent unit (GRU) model. The models were trained on Boston University Radio Speech Corpus, using a minimal set of relevant input features. The models were compared with each other, as well as with state-of-the-art prosody prediction systems from speech synthesis research. Implementation of the proposed prediction model in automatic prosody transplantation is described and the results are analyzed. A perceptual evaluation by native speakers was carried out. Accentedness and comprehensibility ratings of modified and original non-native utterances were compared with each other. The results showed that duration transplantation can lead to the improvements in comprehensibility score. This study lays the groundwork for a fully automatic self-imitation prosody training system and its results can be used to help Korean learners master problematic areas of English prosody, such as sentence stress.Chapter 1. Introduction . 10 1.1 Background. 10 1.2 Research Objective 12 1.3 Research Outline. 15 Chapter 2. Related Works. 16 2.1 Self-imitation Prosody Training. 16 2.1.1 Prosody Transplantation Methods . 18 2.1.2 Effects of Prosody Transplantation on Accentedness Rating 23 2.1.3 Effects of Self-Imitation Learning on Proficiency Rating 26 2.2 Prosody of Korean-accented English Speech 28 2.2.1 Prosodic Systems of Korean and English 28 2.2.2 Common Prosodic Mistakes. 29 2.3 Deep Learning Based Prosody Prediction 34 2.3.1 Deep Learning . 34 2.3.2 Recurrent Neural Networks 35 2.3.2 The Long Short-Term Memory Architecture. 37 2.3.3 Gated Recurrent Units. 39 2.3.4 Prosody Prediction Models 40 Chapter 3. Vowel Duration and Fundamental Frequency Prediction Model 43 3.1 Data 43 3.2. Input Feature Selection. 45 3.3 System Architecture and Training 56 3.4 Results and Evaluation 63 3.4.1 Objective Metrics. 63 3.4.2 Vowel Duration Prediction Models Results. 65 3.4.2 Fundamental Frequency Prediction Models Results 68 3.4.3 Comparison with other models . 68 Chapter 4. Automatic Prosody Transplantation 72 4.1 Data 72 4.2 Transplantation Method. 74 4.3 Perceptual Evaluation 79 4.4 Results 80 Chapter 5. Conclusion. 82 5.1 Summary 82 5.2 Contribution 84 5.3 Limitations 85 5.4 Recommendations for Future Study. 85 References 88 Appendix 96Maste

Speech verification for computer assisted pronunciation training

Computer assisted pronunciation training (CAPT) is an approach that uses computer technology and computer-based resources in teaching and learning pronunciation. It is also part of computer assisted language learning (CALL) technology that has been widely applied to online learning platforms in the past years. This thesis deals with one of the central tasks in CAPT, i.e. speech veri- fication. The goal is to provide a framework that identifies pronunciation errors in speech data of second language (L2) learners and generates feedback with information and instruction for error correction. Furthermore, the framework is supposed to support the adaptation to new L1-L2 language pairs with minimal adjustment and modification. The central result is a novel approach to L2 speech verification, which combines both modern language technologies and linguistic expertise. For pronunciation verification, we select a set of L2 speech data, create alias phonemes from the errors annotated by linguists, then train an acoustic model with mixed L2 and gold standard data and perform HTK phoneme recognition to identify the error phonemes. For prosody verification, FD-PSOLA and Dynamic time warping are both applied to verify the differences in duration, pitch and stress. Feedback is generated for both verifications. Our feedback is presented to learners not only visually as with other existing CAPT systems, but also perceptually by synthesizing the learner’s own audio, e.g. for prosody verification, the gold standard prosody is transplanted onto the learner’s own voice. The framework is self-adaptable under semi-supervision, and requires only a certain amount of mixed gold standard and annotated L2 speech data for boot- strapping. Verified speech data is validated by linguists, annotated in case of wrong verification, and used in the next iteration of training. Mary Annotation Tool (MAT) is developed as an open-source component of MARYTTS for both annotating and validating. To deal with uncertain pauses and interruptions in L2 speech, the silence model in HTK is also adapted, and used in all components of the framework where forced alignment is required. Various evaluations are conducted that help us obtain insights into the applicability and potential of our CAPT system. The pronunciation verification shows high accuracy in both precision and recall, and encourages us to acquire more error-annotated L2 speech data to enhance the trained acoustic model. To test the effect of feedback, a progressive evaluation is carried out and it shows that our perceptual feedback helps learners realize their errors, which they could not otherwise observe from visual feedback and textual instructions. In order to im- prove the user interface, a questionnaire is also designed to collect the learners’ experiences and suggestions.Computer Assisted Pronunciation Training (CAPT) ist ein Ansatz, der mittels Computer und computergestützten Ressourcen das Erlernen der korrekten Aussprache im Fremdsprachenunterricht erleichtert. Dieser Ansatz ist ein Teil der Computer Assisted Language Learning (CALL) Technologie, die seit mehreren Jahren auf Online-Lernplattformen häufig zum Einsatz kommt. Diese Arbeit ist der Sprachverifikation gewidmet, einer der zentralen Aufgaben innerhalb des CAPT. Das Ziel ist, ein Framework zur Identifikation von Aussprachefehlern zu entwickeln fürMenschen, die eine Fremdsprache (L2-Sprache) erlernen. Dabei soll Feedback mit fehlerspezifischen Informationen und Anweisungen für eine richtige Aussprache erzeugt werden. Darüber hinaus soll das Rahmenwerk die Anpassung an neue Sprachenpaare (L1-L2) mit minimalen Adaptationen und Modifikationen unterstützen. Das zentrale Ergebnis ist ein neuartiger Ansatz für die L2-Sprachprüfung, der sowohl auf modernen Sprachtechnologien als auch auf corpuslinguistischen Ansätzen beruht. Für die Ausspracheüberprüfung erstellen wir Alias-Phoneme aus Fehlern, die von Linguisten annotiert wurden. Dann trainieren wir ein akustisches Modell mit gemischten L2- und Goldstandarddaten und führen eine HTK-Phonemerkennung3 aus, um die Fehlerphoneme zu identifizieren. Für die Prosodieüberprüfung werden sowohl FD-PSOLA4 und Dynamic Time Warping angewendet, um die Unterschiede in der Dauer, Tonhöhe und Betonung zwischen dem Gesprochenen und dem Goldstandard zu verifizieren. Feedbacks werden für beide Überprüfungen generiert und den Lernenden nicht nur visuell präsentiert, so wie in anderen vorhandenen CAPT-Systemen, sondern auch perzeptuell vorgestellt. So wird unter anderem für die Prosodieverifikation die Goldstandardprosodie auf die eigene Stimme des Lernenden übergetragen. Zur Anpassung des Frameworks an weitere L1-L2 Sprachdaten muss das System über Maschinelles Lernen trainiert werden. Da es sich um ein semi-überwachtes Lernverfahren handelt, sind nur eine gewisseMenge an gemischten Goldstandardund annotierten L2-Sprachdaten für das Bootstrapping erforderlich. Verifizierte Sprachdaten werden von Linguisten validiert, im Falle einer falschen Verifizierung nochmals annotiert, und bei der nächsten Iteration des Trainings verwendet. Für die Annotation und Validierung wurde das Mary Annotation Tool (MAT) als Open-Source-Komponente von MARYTTS entwickelt. Um mit unsicheren Pausen und Unterbrechungen in der L2-Sprache umzugehen, wurde auch das sogenannte Stillmodell in HTK angepasst und in allen Komponenten des Rahmenwerks verwendet, in denen Forced Alignment erforderlich ist. Unterschiedliche Evaluierungen wurden durchgeführt, um Erkenntnisse über die Anwendungspotenziale und die Beschränkungen des Systems zu gewinnen. Die Ausspracheüberprüfung zeigt eine hohe Genauigkeit sowohl bei der Präzision als auch beim Recall. Dadurch war es möglich weitere fehlerbehaftete L2-Sprachdaten zu verwenden, um somit das trainierte akustische Modell zu verbessern. Um die Wirkung des Feedbacks zu testen, wird eine progressive Auswertung durchgeführt. Das Ergebnis zeigt, dass perzeptive Feedbacks dabei helfen, dass die Lernenden sogar Fehler erkennen, die sie nicht aus visuellen Feedbacks und Textanweisungen beobachten können. Zudem wurden mittels Fragebogen die Erfahrungen und Anregungen der Benutzeroberfläche der Lernenden gesammelt, um das System künftig zu verbessern. 3 Hidden Markov Toolkit 4 Pitch Synchronous Overlap and Ad