RawNet: Fast End-to-End Neural Vocoder

Neural networks based vocoders have recently demonstrated the powerful ability to synthesize high quality speech. These models usually generate samples by conditioning on some spectrum features, such as Mel-spectrum. However, these features are extracted by using speech analysis module including some processing based on the human knowledge. In this work, we proposed RawNet, a truly end-to-end neural vocoder, which use a coder network to learn the higher representation of signal, and an autoregressive voder network to generate speech sample by sample. The coder and voder together act like an auto-encoder network, and could be jointly trained directly on raw waveform without any human-designed features. The experiments on the Copy-Synthesis tasks show that RawNet can achieve the comparative synthesized speech quality with LPCNet, with a smaller model architecture and faster speech generation at the inference step.Comment: Submitted to Interspeech 2019, Graz, Austri

Wavenet based low rate speech coding

Traditional parametric coding of speech facilitates low rate but provides poor reconstruction quality because of the inadequacy of the model used. We describe how a WaveNet generative speech model can be used to generate high quality speech from the bit stream of a standard parametric coder operating at 2.4 kb/s. We compare this parametric coder with a waveform coder based on the same generative model and show that approximating the signal waveform incurs a large rate penalty. Our experiments confirm the high performance of the WaveNet based coder and show that the speech produced by the system is able to additionally perform implicit bandwidth extension and does not significantly impair recognition of the original speaker for the human listener, even when that speaker has not been used during the training of the generative model.Comment: 5 pages, 2 figure

The development of speech coding and the first standard coder for public mobile telephony

This thesis describes in its core chapter (Chapter 4) the original algorithmic and design features of the ??rst coder for public mobile telephony, the GSM full-rate speech coder, as standardized in 1988. It has never been described in so much detail as presented here. The coder is put in a historical perspective by two preceding chapters on the history of speech production models and the development of speech coding techniques until the mid 1980s, respectively. In the epilogue a brief review is given of later developments in speech coding. The introductory Chapter 1 starts with some preliminaries. It is de- ??ned what speech coding is and the reader is introduced to speech coding standards and the standardization institutes which set them. Then, the attributes of a speech coder playing a role in standardization are explained. Subsequently, several applications of speech coders - including mobile telephony - will be discussed and the state of the art in speech coding will be illustrated on the basis of some worldwide recognized standards. Chapter 2 starts with a summary of the features of speech signals and their source, the human speech organ. Then, historical models of speech production which form the basis of di??erent kinds of modern speech coders are discussed. Starting with a review of ancient mechanical models, we will arrive at the electrical source-??lter model of the 1930s. Subsequently, the acoustic-tube models as they arose in the 1950s and 1960s are discussed. Finally the 1970s are reviewed which brought the discrete-time ??lter model on the basis of linear prediction. In a unique way the logical sequencing of these models is exposed, and the links are discussed. Whereas the historical models are discussed in a narrative style, the acoustic tube models and the linear prediction tech nique as applied to speech, are subject to more mathematical analysis in order to create a sound basis for the treatise of Chapter 4. This trend continues in Chapter 3, whenever instrumental in completing that basis. In Chapter 3 the reader is taken by the hand on a guided tour through time during which successive speech coding methods pass in review. In an original way special attention is paid to the evolutionary aspect. Speci??cally, for each newly proposed method it is discussed what it added to the known techniques of the time. After presenting the relevant predecessors starting with Pulse Code Modulation (PCM) and the early vocoders of the 1930s, we will arrive at Residual-Excited Linear Predictive (RELP) coders, Analysis-by-Synthesis systems and Regular- Pulse Excitation in 1984. The latter forms the basis of the GSM full-rate coder. In Chapter 4, which constitutes the core of this thesis, explicit forms of Multi-Pulse Excited (MPE) and Regular-Pulse Excited (RPE) analysis-by-synthesis coding systems are developed. Starting from current pulse-amplitude computation methods in 1984, which included solving sets of equations (typically of order 10-16) two hundred times a second, several explicit-form designs are considered by which solving sets of equations in real time is avoided. Then, the design of a speci??c explicitform RPE coder and an associated eÆcient architecture are described. The explicit forms and the resulting architectural features have never been published in so much detail as presented here. Implementation of such a codec enabled real-time operation on a state-of-the-art singlechip digital signal processor of the time. This coder, at a bit rate of 13 kbit/s, has been selected as the Full-Rate GSM standard in 1988. Its performance is recapitulated. Chapter 5 is an epilogue brie y reviewing the major developments in speech coding technology after 1988. Many speech coding standards have been set, for mobile telephony as well as for other applications, since then. The chapter is concluded by an outlook

Individual and environment-related acoustic-phonetic strategies for communicating in adverse conditions

In many situations it is necessary to produce speech in ‘adverse conditions’: that is, conditions that make speech communication difficult. Research has demonstrated that speaker strategies, as described by a range of acoustic-phonetic measures, can vary both at the individual level and according to the environment, and are argued to facilitate communication. There has been debate as to the environmental specificity of these adaptations, and their effectiveness in overcoming communication difficulty. Furthermore, the manner and extent to which adaptation strategies differ between individuals is not yet well understood. This thesis presents three studies that explore the acoustic-phonetic adaptations of speakers in noisy and degraded communication conditions and their relationship with intelligibility. Study 1 investigated the effects of temporally fluctuating maskers on global acoustic-phonetic measures associated with speech in noise (Lombard speech). The results replicated findings of increased power in the modulation spectrum in Lombard speech, but showed little evidence of adaptation to masker fluctuations via the temporal envelope. Study 2 collected a larger corpus of semi-spontaneous communicative speech in noise and other degradations perturbing specific acoustic dimensions. Speakers showed different adaptations across the environments that were likely suited to overcome noise (steady and temporally fluctuating), restricted spectral and pitch information by a noise-excited vocoder, and a sensorineural hearing loss simulation. Analyses of inter-speaker variation in both studies 1 and 2 showed behaviour was highly variable and some strategy combinations were identified. Study 3 investigated the intelligibility of strategies ‘tailored’ to specific environments and the relationship between intelligibility and speaker acoustics, finding a benefit of tailored speech adaptations and discussing the potential roles of speaker flexibility, adaptation level, and intrinsic intelligibility. The overall results are discussed in relation to models of communication in adverse conditions and a model accounting for individual variability in these conditions is proposed

Recognition of phonemes In a continuous speech stream by means of PARCOR parameters In LPC vocoder

Linear Predictive Coding (LPC) has been used to compress and encode speech signals for digital transmission at a low bit rate. The Partial Correlation (PARCOR) parameter associated with LPC that represents a vocal tract model based on a lattice filter structure is considered for speech recognition. For the same purpose, the use of FIR coefficients and the frequency response of AR model were previously investigated. In this thesis, we investigate the mechanics of the speech production process in human beings and discuss the place and manner of articulation for each of the major phoneme classes of American English. Then we characterize some typical vowel and consonant phonemes by using the eighth order PARCOR parameter associated with LPC.This thesis explores a method to detect phonemes from a continuous stream of speech. The system being developed slides a time window of 16 ms and calculates PARCOR parameters continuously, feeding them to a phoneme classifier. The phoneme classifier is a supervised classifier that requires training. The training uses TIMIT speech database, which contains the recordings of 630 speakers of 8 major dialects of American English. The training data are grouped into the vowel group including phoneme [ae], [iy] and [uw] and the consonant group including [sh] and [f]. After the training, the decision rule is derived. We design two classifiers in this thesis, one is a vowel classifier and the other one is a consonant classifier, both of them use the maximum likelihood decision rule to classify unknown phonemes. The results of classification of vowel and consonant in a one-syllable word are shown in the thesis. The correct classification rate is 65:22% for the vowel group. The correct classification rate is 93:51% for the consonant group. The results indicate that PARCOR parameters have the potential capability to characterize the phoneme

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

Phone-based speech synthesis using neural network with articulatory control.

by Lo Wai Kit.Thesis (M.Phil.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 151-160).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Applications of Speech Synthesis --- p.2Chapter 1.1.1 --- Human Machine Interface --- p.2Chapter 1.1.2 --- Speech Aids --- p.3Chapter 1.1.3 --- Text-To-Speech (TTS) system --- p.4Chapter 1.1.4 --- Speech Dialogue System --- p.4Chapter 1.2 --- Current Status in Speech Synthesis --- p.6Chapter 1.2.1 --- Concatenation Based --- p.6Chapter 1.2.2 --- Parametric Based --- p.7Chapter 1.2.3 --- Articulatory Based --- p.7Chapter 1.2.4 --- Application of Neural Network in Speech Synthesis --- p.8Chapter 1.3 --- The Proposed Neural Network Speech Synthesis --- p.9Chapter 1.3.1 --- Motivation --- p.9Chapter 1.3.2 --- Objectives --- p.9Chapter 1.4 --- Thesis outline --- p.11Chapter 2 --- Linguistic Basics for Speech Synthesis --- p.12Chapter 2.1 --- Relations between Linguistic and Speech Synthesis --- p.12Chapter 2.2 --- Basic Phonology and Phonetics --- p.14Chapter 2.2.1 --- Phonology --- p.14Chapter 2.2.2 --- Phonetics --- p.15Chapter 2.2.3 --- Prosody --- p.16Chapter 2.3 --- Transcription Systems --- p.17Chapter 2.3.1 --- The Employed Transcription System --- p.18Chapter 2.4 --- Cantonese Phonology --- p.20Chapter 2.4.1 --- Some Properties of Cantonese --- p.20Chapter 2.4.2 --- Initial --- p.21Chapter 2.4.3 --- Final --- p.23Chapter 2.4.4 --- Lexical Tone --- p.25Chapter 2.4.5 --- Variations --- p.26Chapter 2.5 --- The Vowel Quadrilaterals --- p.29Chapter 3 --- Speech Synthesis Technology --- p.32Chapter 3.1 --- The Human Speech Production --- p.32Chapter 3.2 --- Important Issues in Speech Synthesis System --- p.34Chapter 3.2.1 --- Controllability --- p.34Chapter 3.2.2 --- Naturalness --- p.34Chapter 3.2.3 --- Complexity --- p.35Chapter 3.2.4 --- Information Storage --- p.35Chapter 3.3 --- Units for Synthesis --- p.37Chapter 3.4 --- Type of Synthesizer --- p.40Chapter 3.4.1 --- Copy Concatenation --- p.40Chapter 3.4.2 --- Vocoder --- p.41Chapter 3.4.3 --- Articulatory Synthesis --- p.44Chapter 4 --- Neural Network Speech Synthesis with Articulatory Control --- p.47Chapter 4.1 --- Neural Network Approximation --- p.48Chapter 4.1.1 --- The Approximation Problem --- p.48Chapter 4.1.2 --- Network Approach for Approximation --- p.49Chapter 4.2 --- Artificial Neural Network for Phone-based Speech Synthesis --- p.53Chapter 4.2.1 --- Network Approximation for Speech Signal Synthesis --- p.53Chapter 4.2.2 --- Feed forward Backpropagation Neural Network --- p.56Chapter 4.2.3 --- Radial Basis Function Network --- p.58Chapter 4.2.4 --- Parallel Operating Synthesizer Networks --- p.59Chapter 4.3 --- Template Storage and Control for the Synthesizer Network --- p.61Chapter 4.3.1 --- Implicit Template Storage --- p.61Chapter 4.3.2 --- Articulatory Control Parameters --- p.61Chapter 4.4 --- Summary --- p.65Chapter 5 --- Prototype Implementation of the Synthesizer Network --- p.66Chapter 5.1 --- Implementation of the Synthesizer Network --- p.66Chapter 5.1.1 --- Network Architectures --- p.68Chapter 5.1.2 --- Spectral Templates for Training --- p.74Chapter 5.1.3 --- System requirement --- p.76Chapter 5.2 --- Subjective Listening Test --- p.79Chapter 5.2.1 --- Sample Selection --- p.79Chapter 5.2.2 --- Test Procedure --- p.81Chapter 5.2.3 --- Result --- p.83Chapter 5.2.4 --- Analysis --- p.86Chapter 5.3 --- Summary --- p.88Chapter 6 --- Simplified Articulatory Control for the Synthesizer Network --- p.89Chapter 6.1 --- Coarticulatory Effect in Speech Production --- p.90Chapter 6.1.1 --- Acoustic Effect --- p.90Chapter 6.1.2 --- Prosodic Effect --- p.91Chapter 6.2 --- Control in various Synthesis Techniques --- p.92Chapter 6.2.1 --- Copy Concatenation --- p.92Chapter 6.2.2 --- Formant Synthesis --- p.93Chapter 6.2.3 --- Articulatory synthesis --- p.93Chapter 6.3 --- Articulatory Control Model based on Vowel Quad --- p.94Chapter 6.3.1 --- Modeling of Variations with the Articulatory Control Model --- p.95Chapter 6.4 --- Voice Correspondence : --- p.97Chapter 6.4.1 --- For Nasal Sounds ´ؤ Inter-Network Correspondence --- p.98Chapter 6.4.2 --- In Flat-Tongue Space - Intra-Network Correspondence --- p.101Chapter 6.5 --- Summary --- p.108Chapter 7 --- Pause Duration Properties in Cantonese Phrases --- p.109Chapter 7.1 --- The Prosodic Feature - Inter-Syllable Pause --- p.110Chapter 7.2 --- Experiment for Measuring Inter-Syllable Pause of Cantonese Phrases --- p.111Chapter 7.2.1 --- Speech Material Selection --- p.111Chapter 7.2.2 --- Experimental Procedure --- p.112Chapter 7.2.3 --- Result --- p.114Chapter 7.3 --- Characteristics of Inter-Syllable Pause in Cantonese Phrases --- p.117Chapter 7.3.1 --- Pause Duration Characteristics for Initials after Pause --- p.117Chapter 7.3.2 --- Pause Duration Characteristic for Finals before Pause --- p.119Chapter 7.3.3 --- General Observations --- p.119Chapter 7.3.4 --- Other Observations --- p.121Chapter 7.4 --- Application of Pause-duration Statistics to the Synthesis System --- p.124Chapter 7.5 --- Summary --- p.126Chapter 8 --- Conclusion and Further Work --- p.127Chapter 8.1 --- Conclusion --- p.127Chapter 8.2 --- Further Extension Work --- p.130Chapter 8.2.1 --- Regularization Network Optimized on ISD --- p.130Chapter 8.2.2 --- Incorporation of Non-Articulatory Parameters to Control Space --- p.130Chapter 8.2.3 --- Experiment on Other Prosodic Features --- p.131Chapter 8.2.4 --- Application of Voice Correspondence to Cantonese Coda Discrim- ination --- p.131Chapter A --- Cantonese Initials and Finals --- p.132Chapter A.1 --- Tables of All Cantonese Initials and Finals --- p.132Chapter B --- Using Distortion Measure as Error Function in Neural Network --- p.135Chapter B.1 --- Formulation of Itakura-Saito Distortion Measure for Neural Network Error Function --- p.135Chapter B.2 --- Formulation of a Modified Itakura-Saito Distortion (MISD) Measure for Neural Network Error Function --- p.137Chapter C --- Orthogonal Least Square Algorithm for RBFNet Training --- p.138Chapter C.l --- Orthogonal Least Squares Learning Algorithm for Radial Basis Function Network Training --- p.138Chapter D --- Phrase Lists --- p.140Chapter D.1 --- Two-Syllable Phrase List for the Pause Duration Experiment --- p.140Chapter D.1.1 --- 兩字詞 --- p.140Chapter D.2 --- Three/Four-Syllable Phrase List for the Pause Duration Experiment --- p.144Chapter D.2.1 --- 片語 --- p.14