Hierarchical Representation and Estimation of Prosody using Continuous Wavelet Transform

Prominences and boundaries are the essential constituents of prosodic struc- ture in speech. They provide for means to chunk the speech stream into linguis- tically relevant units by providing them with relative saliences and demarcating them within utterance structures. Prominences and boundaries have both been widely used in both basic research on prosody as well as in text-to-speech syn- thesis. However, there are no representation schemes that would provide for both estimating and modelling them in a unified fashion. Here we present an unsupervised unified account for estimating and representing prosodic promi- nences and boundaries using a scale-space analysis based on continuous wavelet transform. The methods are evaluated and compared to earlier work using the Boston University Radio News corpus. The results show that the proposed method is comparable with the best published supervised annotation methods.Peer reviewe

Fundamental frequency modelling: an articulatory perspective with target approximation and deep learning

Current statistical parametric speech synthesis (SPSS) approaches typically aim at state/frame-level acoustic modelling, which leads to a problem of frame-by-frame independence. Besides that, whichever learning technique is used, hidden Markov model (HMM), deep neural network (DNN) or recurrent neural network (RNN), the fundamental idea is to set up a direct mapping from linguistic to acoustic features. Although progress is frequently reported, this idea is questionable in terms of biological plausibility. This thesis aims at addressing the above issues by integrating dynamic mechanisms of human speech production as a core component of F0 generation and thus developing a more human-like F0 modelling paradigm. By introducing an articulatory F0 generation model – target approximation (TA) – between text and speech that controls syllable-synchronised F0 generation, contextual F0 variations are processed in two separate yet integrated stages: linguistic to motor, and motor to acoustic. With the goal of demonstrating that human speech movement can be considered as a dynamic process of target approximation and that the TA model is a valid F0 generation model to be used at the motor-to-acoustic stage, a TA-based pitch control experiment is conducted first to simulate the subtle human behaviour of online compensation for pitch-shifted auditory feedback. Then, the TA parameters are collectively controlled by linguistic features via a deep or recurrent neural network (DNN/RNN) at the linguistic-to-motor stage. We trained the systems on a Mandarin Chinese dataset consisting of both statements and questions. The TA-based systems generally outperformed the baseline systems in both objective and subjective evaluations. Furthermore, the amount of required linguistic features were reduced first to syllable level only (with DNN) and then with all positional information removed (with RNN). Fewer linguistic features as input with limited number of TA parameters as output led to less training data and lower model complexity, which in turn led to more efficient training and faster synthesis

Intonation modelling using a muscle model and perceptually weighted matching pursuit

We propose a physiologically based intonation model using perceptual relevance. Motivated by speech synthesis from a speech-to-speech translation (S2ST) point of view, we aim at a language independent way of modelling intonation. The model presented in this paper can be seen as a generalisation of the command response (CR) model, albeit with the same modelling power. It is an additive model which decomposes intonation contours into a sum of critically damped system impulse responses. To decompose the intonation contour, we use a weighted correlation based atom decomposition algorithm (WCAD) built around a matching pursuit framework. The algorithm allows for an arbitrary precision to be reached using an iterative procedure that adds more elementary atoms to the model. Experiments are presented demonstrating that this generalised CR (GCR) model is able to model intonation as would be expected. Experiments also show that the model produces a similar number of parameters or elements as the CR model. We conclude that the GCR model is appropriate as an engineering solution for modelling prosody, and hope that it is a contribution to a deeper scientific understanding of the neurobiological process of intonation

Modeling of Polish Intonation for Statistical-Parametric Speech Synthesis

Wydział NeofilologiiBieżąca praca prezentuje próbę budowy neurobiologicznie umotywowanego modelu mapowań pomiędzy wysokopoziomowymi dyskretnymi kategoriami lingwistycznymi a ciągłym sygnałem częstotliwości podstawowej w polskiej neutralnej mowie czytanej, w oparciu o konwolucyjne sieci neuronowe. Po krótkim wprowadzeniu w problem badawczy w kontekście intonacji, syntezy mowy oraz luki pomiędzy fonetyką a fonologią, praca przedstawia opis uczenia modelu na podstawie specjalnego korpusu mowy oraz ewaluację naturalności konturu F0 generowanego przez wyuczony model za pomocą eksperymentów percepcyjnych typu ABX oraz MOS przy użyciu specjalnie w tym celu zbudowanego resyntezatora Neural Source Filter. Następnie, prezentowane są wyniki eksploracji fonologiczno-fonetycznych mapowań wyuczonych przez model. W tym celu wykorzystana została jedna z tzw. metod wyjaśniających dla sztucznej inteligencji – Layer-wise Relevance Propagation. W pracy przedstawione zostały wyniki powstałej na tej podstawie obszernej analizy ilościowej istotności dla konturu częstotliwości podstawowej każdej z 1297 specjalnie wygenerowanych lingwistycznych kategorii wejściowych modelu oraz ich wielorakich grupowań na różnorodnych poziomach abstrakcji. Pracę kończy dogłębna analiza oraz interpretacja uzyskanych wyników oraz rozważania na temat mocnych oraz słabych stron zastosowanych metod, a także lista proponowanych usprawnień.This work presents an attempt to build a neurobiologically inspired Convolutional Neural Network-based model of the mappings between discrete high-level linguistic categories into a continuous signal of fundamental frequency in Polish neutral read speech. After a brief introduction of the current research problem in the context of intonation, speech synthesis and the phonetic-phonology gap, the work goes on to describe the training of the model on a special speech corpus, and an evaluation of the naturalness of the F0 contour produced by the trained model through ABX and MOS perception experiments conducted with help of a specially built Neural Source Filter resynthesizer. Finally, an in-depth exploration of the phonology-to-phonetics mappings learned by the model is presented; the Layer-wise Relevance Propagation explainability method was used to perform an extensive quantitative analysis of the relevance of 1297 specially engineered linguistic input features and their groupings at various levels of abstraction for the specific contours of the fundamental frequency. The work ends with an in-depth interpretation of these results and a discussion of the advantages and disadvantages of the current method, and lists a number of potential future improvements.Badania przedstawione w pracy zostały cz˛e´sciowo zrealizowane w ramach grantu badawczego Harmonia nr UMO-2014/14/M/HS2/00631 przyznanego przez Narodowe Centrum Nauki

Intonation Modelling for Speech Synthesis and Emphasis Preservation

Speech-to-speech translation is a framework which recognises speech in an input language, translates it to a target language and synthesises speech in this target language. In such a system, variations in the speech signal which are inherent to natural human speech are lost, as the information goes through the different building blocks of the translation process. The work presented in this thesis addresses aspects of speech synthesis which are lost in traditional speech-to-speech translation approaches. The main research axis of this thesis is the study of prosody for speech synthesis and emphasis preservation. A first investigation of regional accents of spoken French is carried out to understand the sensitivity of native listeners with respect to accented speech synthesis. Listening tests show that standard adaptation methods for speech synthesis are not sufficient for listeners to perceive accentedness. On the other hand, combining adaptation with original prosody allows perception of accents. Addressing the need of a more suitable prosody model, a physiologically plausible intonation model is proposed. Inspired by the command-response model, it has basic components, which can be related to muscle responses to nerve impulses. These components are assumed to be a representation of muscle control of the vocal folds. A motivation for such a model is its theoretical language independence, based on the fact that humans share the same vocal apparatus. An automatic parameter extraction method which integrates a perceptually relevant measure is proposed with the model. This approach is evaluated and compared with the standard command-response model. Two corpora including sentences with emphasised words are presented, in the context of the SIWIS project. The first is a multilingual corpus with speech from multiple speaker; the second is a high quality speech synthesis oriented corpus from a professional speaker. Two broad uses of the model are evaluated. The first shows that it is difficult to predict model parameters; however the second shows that parameters can be transferred in the context of emphasis synthesis. A relation between model parameters and linguistic features such as stress and accent is demonstrated. Similar observations are made between the parameters and emphasis. Following, we investigate the extraction of atoms in emphasised speech and their transfer in neutral speech, which turns out to elicit emphasis perception. Using clustering methods, this is extended to the emphasis of other words, using linguistic context. This approach is validated by listening tests, in the case of English