Parole | EPFL Graph Search

La parole est le langage articulé humain (même si des études ethologiques et philosophiques sont en cours pour déterminer si d’autres animaux pourraient être considérés comme dotés de parole, notamment les perroquets) destiné à communiquer la pensée, et est à distinguer des communications orales diverses, comme les cris, les alertes ou les gémissements. « Articuler la parole » consiste à former des signes audibles, les syllabes, formant les mots qui constituent des symboles. Métaphoriquement, la parole est devenue toute communication s'adressant à l'esprit (parler avec le regard, la gestuelle, le silence). L'étymologie du mot parole est la même que celle du mot parabole, en grec le mot signifie « rapprochement, comparaison ». La parole est le langage incarné de l'Homme. La parole est singulière et opère un acte de langage qui s'adresse à un interlocuteur, éventuellement soi-même, mentalement, ou à un support par l'écrit par exemple. La parole permet d'exprimer des besoins, pensées,

Multi-parametric source-filter separation of speech and prosodic voice restoration

Olaf Schleusing

In this thesis, methods and models are developed and presented aiming at the estimation, restoration and transformation of the characteristics of human speech. During a first period of the thesis, a concept was developed that allows restoring prosodic voice features and reconstruct more natural sounding speech from pathological voices using a multi-resolution approach. Inspired from observations with respect to this approach, the necessity of a novel method for the separation of speech into voice source and articulation components emerged in order to improve the perceptive quality of the restored speech signal. This work subsequently represents the main part of this work and therefore is presented first in this thesis. The proposed method is evaluated on synthetic, physically modelled, healthy and pathological speech. A robust, separate representation of source and filter characteristics has applications in areas that go far beyond the reconstruction of alaryngeal speech. It is potentially useful for efficient speech coding, voice biometrics, emotional speech synthesis, remote and/or non-invasive voice disorder diagnosis, etc. A key aspect of the voice restoration method is the reliable separation of the speech signal into voice source and articulation for it is mostly the voice source that requires replacement or enhancement in alaryngeal speech. Observations during the evaluation of above method highlighted that this separation is insufficient with currently known methods. Therefore, the main part of this thesis is concerned with the modelling of voice and vocal tract and the estimation of the respective model parameters. Most methods for joint source filter estimation known today represent a compromise between model complexity, estimation feasibility and estimation efficiency. Typically, single-parametric models are used to represent the source for the sake of tractable optimization or multi-parametric models are estimated using inefficient grid searches over the entire parameter space. The novel method presented in this work proposes advances in the direction of efficiently estimating and fitting multi-parametric source and filter models to healthy and pathological speech signals, resulting in a more reliable estimation of voice source and especially vocal tract coefficients. In particular, the proposed method is exhibits a largely reduced bias in the estimated formant frequencies and bandwidths over a large variety of experimental conditions such as environmental noise, glottal jitter, fundamental frequency, voice types and glottal noise. The methods appears to be especially robust to environmental noise and improves the separation of deterministic voice source components from the articulation. Alaryngeal speakers often have great difficulty at producing intelligible, not to mention prosodic, speech. Despite great efforts and advances in surgical and rehabilitative techniques, currently known methods, devices and modes of speech rehabilitation leave pathological speakers with a lack in the ability to control key aspects of their voice. The proposed multiresolution approach presented at the end of this thesis provides alaryngeal speakers an intuitive manner to increase prosodic features in their speech by reconstructing a more intelligible, more natural and more prosodic voice. The proposed method is entirely non-invasive. Key prosodic cues are reconstructed and enhanced at different temporal scales by inducing additional volatility estimated from other, still intact, speech features. The restored voice source is thus controllable in an intuitive way by the alaryngeal speaker. Despite the above mentioned advantages there is also a weak point of the proposed joint source-filter estimation method to be mentioned. The proposed method exhibits a susceptibility to modelling errors of the glottal source. On the other hand, the proposed estimation framework appears to be well suited for future research on exactly this topic. A logical continuation of this work is the leverage the efficiency and reliability of the proposed method for the development of new, more accurate glottal source models.

EPFL2012

Novel Methods for Incorporating Prior Knowledge for Automatic Speech Assessment

Subrahmanya Pavankumar Dubagunta

Speech signal conveys several kinds of information such as a message, speaker identity, emotional state of the speaker and social state of the speaker. Automatic speech assessment is a broad area that refers to using automatic methods to predict human judgements regarding different kinds of information conveyed in speech, such as intelligibility of the spoken message, dialect and fluency of the speaker. Unlike other speech technology areas, such as automatic speech recognition, text-to-speech synthesis and automatic speaker recognition, automatic speech assessment is an emerging direction of research. One of the challenges in this field is that there is no single method or framework that scales across diverse speech assessment tasks. Thus, this thesis takes a broader outlook and focuses on prior knowledge incorporation for diverse data-driven speech assessment problems.First, we focus on the development of end-to-end acoustic modelling methods for non-verbal cue-based speech assessment. More precisely, we develop neural network-based methods that can integrate prior knowledge about speech production to learn to assess speech from raw waveform. We validate the developed methods through investigations on several speech assessment tasks, viz. dialect identification, depression detection and speech fluency rating prediction.Second, we focus on advancing a recently proposed phone posterior feature-based intelligibility estimation technique. Specifically, to enhance phone posterior probability estimation, we propose two novel approaches to incorporate linguistic segment level knowledge during the training of neural networks through estimation of confidence measures. We validate the two proposed approaches through automatic speech recognition and dysarthric speech intelligibility assessment studies.Finally, in the context of privacy preservation, we develop a signal processing-based speech pseudonymization approach that alters voice source information and vocal tract system information based on prior knowledge to obfuscate the speaker identity, while retaining intelligibility, i.e. the phones and words remain recognizable. We validate the proposed pseudonymization approach through listening experiments and automatic evaluations.

EPFL2021