Automatic Conversion of Emotions in Speech within a Speaker Independent Framework

Emotions in speech are a fundamental part of a natural dialog. In everyday life, vocal interaction with people often implies emotions as an intrinsic part of the conversation to a greater or lesser extent. Thus, the inclusion of emotions in human-machine dialog systems is crucial to achieve an acceptable degree of naturalness in the communication. This thesis focuses on automatic emotion conversion of speech, a technique whose aim is to transform an utterance produced in neutral style to a certain emotion state in a speaker independent context. Conversion of emotions represents a challenge in the sense that emotions a affect significantly all the parts of the human vocal production system, and in the conversion process all these factors must be taken into account carefully. The techniques used in the literature are based on voice conversion approaches, with minor modifications to create the sensation of emotion. In this thesis, the idea of voice conversion systems is used as well, but the usual regression process is divided in a two-step procedure that provides additional speaker normalization to remove the intrinsic speaker dependency of this kind of systems, using vocal tract length normalization as a pre-processing technique. In addition, a new method to convert the duration trend of the utterance and the intonation contour is proposed, taking into account the contextual information

Voice conversion versus speaker verification: an overview

A speaker verification system automatically accepts or rejects a claimed identity of a speaker based on a speech sample. Recently, a major progress was made in speaker verification which leads to mass market adoption, such as in smartphone and in online commerce for user authentication. A major concern when deploying speaker verification technology is whether a system is robust against spoofing attacks. Speaker verification studies provided us a good insight into speaker characterization, which has contributed to the progress of voice conversion technology. Unfortunately, voice conversion has become one of the most easily accessible techniques to carry out spoofing attacks; therefore, presents a threat to speaker verification systems. In this paper, we will briefly introduce the fundamentals of voice conversion and speaker verification technologies. We then give an overview of recent spoofing attack studies under different conditions with a focus on voice conversion spoofing attack. We will also discuss anti-spoofing attack measures for speaker verification.Published versio

Robust Speaker-Adaptive HMM-Based Text-to-Speech Synthesis

AbstractWe present an algorithm for solving the radiative transfer problem on massively parallel computers using adaptive mesh refinement and domain decomposition. The solver is based on the method of characteristics which requires an adaptive raytracer that integrates the equation of radiative transfer. The radiation field is split into local and global components which are handled separately to overcome the non-locality problem. The solver is implemented in the framework of the magneto-hydrodynamics code FLASH and is coupled by an operator splitting step. The goal is the study of radiation in the context of star formation simulations with a focus on early disc formation and evolution. This requires a proper treatment of radiation physics that covers both the optically thin as well as the optically thick regimes and the transition region in particular. We successfully show the accuracy and feasibility of our method in a series of standard radiative transfer problems and two 3D collapse simulations resembling the early stages of protostar and disc formation

A Review of Deep Learning Techniques for Speech Processing

The field of speech processing has undergone a transformative shift with the advent of deep learning. The use of multiple processing layers has enabled the creation of models capable of extracting intricate features from speech data. This development has paved the way for unparalleled advancements in speech recognition, text-to-speech synthesis, automatic speech recognition, and emotion recognition, propelling the performance of these tasks to unprecedented heights. The power of deep learning techniques has opened up new avenues for research and innovation in the field of speech processing, with far-reaching implications for a range of industries and applications. This review paper provides a comprehensive overview of the key deep learning models and their applications in speech-processing tasks. We begin by tracing the evolution of speech processing research, from early approaches, such as MFCC and HMM, to more recent advances in deep learning architectures, such as CNNs, RNNs, transformers, conformers, and diffusion models. We categorize the approaches and compare their strengths and weaknesses for solving speech-processing tasks. Furthermore, we extensively cover various speech-processing tasks, datasets, and benchmarks used in the literature and describe how different deep-learning networks have been utilized to tackle these tasks. Additionally, we discuss the challenges and future directions of deep learning in speech processing, including the need for more parameter-efficient, interpretable models and the potential of deep learning for multimodal speech processing. By examining the field's evolution, comparing and contrasting different approaches, and highlighting future directions and challenges, we hope to inspire further research in this exciting and rapidly advancing field

Exemplar-based voice conversion using non-negative spectrogram deconvolution

In the traditional voice conversion, converted speech is gener-ated using statistical parametric models (for example Gaussian mix-ture model) whose parameters are estimated from parallel training utterances. A well-known problem of the statistical parametric meth-ods is that statistical average in parameter estimation results in the over-smoothing of the speech parameter trajectories, and thus leads to low conversion quality. Inspired by recent success of so-called exemplar-based methods in robust speech recognition, we propose a voice conversion system based on non-negative spectrogram de-convolution with similar ideas. Exemplars, which are able to cap-ture temporal context, are employed to generate converted speech spectrogram convolutely. The exemplar-based approach is seen as a data-driven, non-parametric approach as an alternative to the tradi-tional parametric approaches to voice conversion. Experiments on VOICES database indicate that the proposed method outperforms the conventional joint density Gaussian mixture model by a wide margin in terms of both objective and subjective evaluations. Index Terms — Voice conversion, exemplar, non-negative ma-trix factorization, non-negative matrix deconvolution, temporal in-formation 1

Suprasegmental representations for the modeling of fundamental frequency in statistical parametric speech synthesis

Statistical parametric speech synthesis (SPSS) has seen improvements over recent years, especially in terms of intelligibility. Synthetic speech is often clear and understandable, but it can also be bland and monotonous. Proper generation of natural speech prosody is still a largely unsolved problem. This is relevant especially in the context of expressive audiobook speech synthesis, where speech is expected to be fluid and captivating. In general, prosody can be seen as a layer that is superimposed on the segmental (phone) sequence. Listeners can perceive the same melody or rhythm in different utterances, and the same segmental sequence can be uttered with a different prosodic layer to convey a different message. For this reason, prosody is commonly accepted to be inherently suprasegmental. It is governed by longer units within the utterance (e.g. syllables, words, phrases) and beyond the utterance (e.g. discourse). However, common techniques for the modeling of speech prosody - and speech in general - operate mainly on very short intervals, either at the state or frame level, in both hidden Markov model (HMM) and deep neural network (DNN) based speech synthesis. This thesis presents contributions supporting the claim that stronger representations of suprasegmental variation are essential for the natural generation of fundamental frequency for statistical parametric speech synthesis. We conceptualize the problem by dividing it into three sub-problems: (1) representations of acoustic signals, (2) representations of linguistic contexts, and (3) the mapping of one representation to another. The contributions of this thesis provide novel methods and insights relating to these three sub-problems. In terms of sub-problem 1, we propose a multi-level representation of f0 using the continuous wavelet transform and the discrete cosine transform, as well as a wavelet-based decomposition strategy that is linguistically and perceptually motivated. In terms of sub-problem 2, we investigate additional linguistic features such as text-derived word embeddings and syllable bag-of-phones and we propose a novel method for learning word vector representations based on acoustic counts. Finally, considering sub-problem 3, insights are given regarding hierarchical models such as parallel and cascaded deep neural networks