A Tutorial on Speech Synthesis Models

For Speech Synthesis, the understanding of the physical and mathematical models of speech is essential. Hence, Speech Modeling is a large field, and is well documented in literature. The aim in this paper is to provide a background review of several speech models used in speech synthesis, specifically the Source Filter Model, Linear Prediction Model, Sinusoidal Model, and Harmonic/Noise Model. The most important models of speech signals will be described starting from the earlier ones up until the last ones, in order to highlight major improvements over these models. It would be desirable a parametric model of speech, that is relatively simple, flexible, high quality, and robust in re-synthesis. Emphasis will be given in Harmonic / Noise Model, since it seems to be more promising and robust model of speech. (C) 2015 The Authors. Published by Elsevier B.V

AN EFFICIENT SPEECH GENERATIVE MODEL BASED ON DETERMINISTIC/STOCHASTIC SEPARATION OF SPECTRAL ENVELOPES

The paper presents a speech generative model that provides an efficient way of generating speech waveform from its amplitude spectral envelopes. The model is based on hybrid speech representation that includes deterministic (harmonic) and stochastic (noise) components. The main idea behind the approach originates from the fact that speech signal has a determined spectral structure that is statistically bound with deterministic/stochastic energy distribution in the spectrum. The performance of the model is evaluated using an experimental low-bitrate wide-band speech coder. The quality of reconstructed speech is evaluated using objective and subjective methods. Two objective quality characteristics were calculated: Modified Bark Spectral Distortion (MBSD) and Perceptual Evaluation of Speech Quality (PESQ). Narrow-band and wide-band versions of the proposed solution were compared with MELP (Mixed Excitation Linear Prediction) speech coder and AMR (Adaptive Multi-Rate) speech coder, respectively. The speech base of two female and two male speakers were used for testing. The performed tests show that overall performance of the proposed approach is speaker-dependent and it is better for male voices. Supposedly, this difference indicates the influence of pitch highness on separation accuracy. In that way, using the proposed approach in experimental speech compression system provides decent MBSD values and comparable PESQ values with AMR speech coder at 6,6 kbit/s. Additional subjective listening testsdemonstrate that the implemented coding system retains phonetic content and speaker’s identity. It proves consistency of the proposed approach.The paper presents a speech generative model that provides an efficient way of generating speech waveform from its amplitude spectral envelopes. The model is based on hybrid speech representation that includes deterministic (harmonic) and stochastic (noise) components. The main idea behind the approach originates from the fact that speech signal has a determined spectral structure that is statistically bound with deterministic/stochastic energy distribution in the spectrum. The performance of the model is evaluated using an experimental low-bitrate wide-band speech coder. The quality of reconstructed speech is evaluated using objective and subjective methods. Two objective quality characteristics were calculated: Modified Bark Spectral Distortion (MBSD) and Perceptual Evaluation of Speech Quality (PESQ). Narrow-band and wide-band versions of the proposed solution were compared with MELP (Mixed Excitation Linear Prediction) speech coder and AMR (Adaptive Multi-Rate) speech coder, respectively. The speech base of two female and two male speakers were used for testing. The performed tests show that overall performance of the proposed approach is speaker-dependent and it is better for male voices. Supposedly, this difference indicates the influence of pitch highness on separation accuracy. In that way, using the proposed approach in experimental speech compression system provides decent MBSD values and comparable PESQ values with AMR speech coder at 6,6 kbit/s. Additional subjective listening testsdemonstrate that the implemented coding system retains phonetic content and speaker’s identity. It proves consistency of the proposed approach

A review of differentiable digital signal processing for music and speech synthesis

The term “differentiable digital signal processing” describes a family of techniques in which loss function gradients are backpropagated through digital signal processors, facilitating their integration into neural networks. This article surveys the literature on differentiable audio signal processing, focusing on its use in music and speech synthesis. We catalogue applications to tasks including music performance rendering, sound matching, and voice transformation, discussing the motivations for and implications of the use of this methodology. This is accompanied by an overview of digital signal processing operations that have been implemented differentiably, which is further supported by a web book containing practical advice on differentiable synthesiser programming (https://intro2ddsp.github.io/). Finally, we highlight open challenges, including optimisation pathologies, robustness to real-world conditions, and design trade-offs, and discuss directions for future research

In search of the optimal acoustic features for statistical parametric speech synthesis

In the Statistical Parametric Speech Synthesis (SPSS) paradigm, speech is generally represented as acoustic features and the waveform is generated by a vocoder. A comprehensive summary of state-of-the-art vocoding techniques is presented, highlighting their characteristics, advantages, and drawbacks, primarily when used in SPSS. We conclude that state-of-the-art vocoding methods are suboptimal and are a cause of significant loss of quality, even though numerous vocoders have been proposed in the last decade. In fact, it seems that the most complicated methods perform worse than simpler ones based on more robust analysis/synthesis algorithms. Typical methods, based on the source-filter or sinusoidal models, rely on excessive simplifying assumptions. They perform what we call an "extreme decomposition" of speech (e.g., source+filter or sinusoids+ noise), which we believe to be a major drawback. Problems include: difficulties in the estimation of components; modelling of complex non-linear mechanisms; a lack of ground truth. In addition, the statistical dependence that exists between stochastic and deterministic components of speech is not modelled. We start by improving just the waveform generation stage of SPSS, using standard acoustic features. We propose a new method of waveform generation tailored for SPSS, based on neither source-filter separation nor sinusoidal modelling. The proposed waveform generator avoids unnecessary assumptions and decompositions as far as possible, and uses only the fundamental frequency and spectral envelope as acoustic features. A very small speech database is used as a source of base speech signals which are subsequently \reshaped" to match the specifications output by the acoustic model in the SPSS framework. All of this is done without any decomposition, such as source+filter or harmonics+noise. A comprehensive description of the waveform generation process is presented, along with implementation issues. Two SPSS voices, a female and a male, were built to test the proposed method by using a standard TTS toolkit, Merlin. In a subjective evaluation, listeners preferred the proposed waveform generator over a state-of-the-art vocoder, STRAIGHT. Even though the proposed \waveform reshaping" generator generates higher speech quality than STRAIGHT, the improvement is not large enough. Consequently, we propose a new acoustic representation, whose implementation involves feature extraction and waveform generation, i.e., a complete vocoder. The new representation encodes the complex spectrum derived from the Fourier Transform in a way explicitly designed for SPSS, rather than for speech coding or copy-synthesis. The feature set comprises four feature streams describing magnitude spectrum, phase spectrum, and fundamental frequency; all of these are represented by real numbers. It avoids heuristics or unstable methods for phase unwrapping. The new feature extraction does not attempt to decompose the speech structure and thus the "phasiness" and "buzziness" found in a typical vocoder, such as STRAIGHT, is dramatically reduced. Our method works at a lower frame rate than a typical vocoder. To demonstrate the proposed method, two DNN-based voices, a male and a female, were built using the Merlin toolkit. Subjective comparisons were performed with a state-of-the-art baseline. The proposed vocoder substantially outperformed the baseline for both voices and under all configurations tested. Furthermore, several enhancements were made over the original design, which are beneficial for either sound quality or compatibility with other tools. In addition to its use in SPSS, the proposed vocoder is also demonstrated being used for join smoothing in unit selection-based systems, and can be used for voice conversion or automatic speech recognition

Statistical parametric speech synthesis based on sinusoidal models

This study focuses on improving the quality of statistical speech synthesis based on sinusoidal models. Vocoders play a crucial role during the parametrisation and reconstruction process, so we first lead an experimental comparison of a broad range of the leading vocoder types. Although our study shows that for analysis / synthesis, sinusoidal models with complex amplitudes can generate high quality of speech compared with source-filter ones, component sinusoids are correlated with each other, and the number of parameters is also high and varies in each frame, which constrains its application for statistical speech synthesis. Therefore, we first propose a perceptually based dynamic sinusoidal model (PDM) to decrease and fix the number of components typically used in the standard sinusoidal model. Then, in order to apply the proposed vocoder with an HMM-based speech synthesis system (HTS), two strategies for modelling sinusoidal parameters have been compared. In the first method (DIR parameterisation), features extracted from the fixed- and low-dimensional PDM are statistically modelled directly. In the second method (INT parameterisation), we convert both static amplitude and dynamic slope from all the harmonics of a signal, which we term the Harmonic Dynamic Model (HDM), to intermediate parameters (regularised cepstral coefficients (RDC)) for modelling. Our results show that HDM with intermediate parameters can generate comparable quality to STRAIGHT. As correlations between features in the dynamic model cannot be modelled satisfactorily by a typical HMM-based system with diagonal covariance, we have applied and tested a deep neural network (DNN) for modelling features from these two methods. To fully exploit DNN capabilities, we investigate ways to combine INT and DIR at the level of both DNN modelling and waveform generation. For DNN training, we propose to use multi-task learning to model cepstra (from INT) and log amplitudes (from DIR) as primary and secondary tasks. We conclude from our results that sinusoidal models are indeed highly suited for statistical parametric synthesis. The proposed method outperforms the state-of-the-art STRAIGHT-based equivalent when used in conjunction with DNNs. To further improve the voice quality, phase features generated from the proposed vocoder also need to be parameterised and integrated into statistical modelling. Here, an alternative statistical model referred to as the complex-valued neural network (CVNN), which treats complex coefficients as a whole, is proposed to model complex amplitude explicitly. A complex-valued back-propagation algorithm using a logarithmic minimisation criterion which includes both amplitude and phase errors is used as a learning rule. Three parameterisation methods are studied for mapping text to acoustic features: RDC / real-valued log amplitude, complex-valued amplitude with minimum phase and complex-valued amplitude with mixed phase. Our results show the potential of using CVNNs for modelling both real and complex-valued acoustic features. Overall, this thesis has established competitive alternative vocoders for speech parametrisation and reconstruction. The utilisation of proposed vocoders on various acoustic models (HMM / DNN / CVNN) clearly demonstrates that it is compelling to apply them for the parametric statistical speech synthesis

Voice source modelling using deep neural networks for statistical parametric speech synthesis

Publication in the conference proceedings of EUSIPCO, Lisbon, Portugal, 201

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