Speech impairment in Parkinson’s disease: acoustic analysis of unvoiced consonants in Italian native speakers

The study of the influence of Parkinson’s Disease (PD) on vocal signals has received much attention over the last decades. Increasing interest has been devoted to articulation and acoustic characterization of different phonemes. Method: In this study we propose the analysis of the Transition Regions (TR) of specific phonetic groups to model the loss of motor control and the difficulty to start/stop movements, typical of PD patients. For this purpose, we extracted 60 features from pre-processed vocal signals and used them as input to several machine learning models. We employed two data sets, containing samples from Italian native speakers, for training and testing. The first dataset - 28 PD patients and 22 Healthy Control (HC) - included recordings in optimal conditions, while in the second one - 26 PD patients and 18 HC- signals were collected at home, using non-professional microphones. Results: We optimized two support vector machine models for the application in controlled noise conditions and home environments, achieving 98% ± 1.1 and 88% ± 2.8 accuracy in 10-fold cross-validation, respectively. Conclusion: This study confirms the high capability of the TRs to discriminate between PD patients and healthy controls, and the feasibility of automatic PD assessment using voice recordings. Moreover, the promising performance of the implemented model discloses the option of voice processing using low-cost devices and domestic recordings, possibly self-managed by the patients themselves

Speech Impairment in Parkinson's Disease: Acoustic Analysis of Unvoiced Consonants in Italian Native Speakers

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Acoustic Approaches to Gender and Accent Identification

There has been considerable research on the problems of speaker and language recognition from samples of speech. A less researched problem is that of accent recognition. Although this is a similar problem to language identification, di�erent accents of a language exhibit more fine-grained di�erences between classes than languages. This presents a tougher problem for traditional classification techniques. In this thesis, we propose and evaluate a number of techniques for gender and accent classification. These techniques are novel modifications and extensions to state of the art algorithms, and they result in enhanced performance on gender and accent recognition. The first part of the thesis focuses on the problem of gender identification, and presents a technique that gives improved performance in situations where training and test conditions are mismatched. The bulk of this thesis is concerned with the application of the i-Vector technique to accent identification, which is the most successful approach to acoustic classification to have emerged in recent years. We show that it is possible to achieve high accuracy accent identification without reliance on transcriptions and without utilising phoneme recognition algorithms. The thesis describes various stages in the development of i-Vector based accent classification that improve the standard approaches usually applied for speaker or language identification, which are insu�cient. We demonstrate that very good accent identification performance is possible with acoustic methods by considering di�erent i-Vector projections, frontend parameters, i-Vector configuration parameters, and an optimised fusion of the resulting i-Vector classifiers we can obtain from the same data. We claim to have achieved the best accent identification performance on the test corpus for acoustic methods, with up to 90% identification rate. This performance is even better than previously reported acoustic-phonotactic based systems on the same corpus, and is very close to performance obtained via transcription based accent identification. Finally, we demonstrate that the utilization of our techniques for speech recognition purposes leads to considerably lower word error rates. Keywords: Accent Identification, Gender Identification, Speaker Identification, Gaussian Mixture Model, Support Vector Machine, i-Vector, Factor Analysis, Feature Extraction, British English, Prosody, Speech Recognition

Acoustic model selection for recognition of regional accented speech

Accent is cited as an issue for speech recognition systems. Our experiments showed that the ASR word error rate is up to seven times greater for accented speech compared with standard British English. The main objective of this research is to develop Automatic Speech Recognition (ASR) techniques that are robust to accent variation. We applied different acoustic modelling techniques to compensate for the effects of regional accents on the ASR performance. For conventional GMM-HMM based ASR systems, we showed that using a small amount of data from a test speaker to choose an accent dependent model using an accent identification system, or building a model using the data from N neighbouring speakers in AID space, will result in superior performance compared to that obtained with unsupervised or supervised speaker adaptation. In addition we showed that using a DNN-HMM rather than a GMM-HMM based acoustic model would improve the recognition accuracy considerably. Even if we apply two stages of accent followed by speaker adaptation to the GMM-HMM baseline system, the GMM-HMM based system will not outperform the baseline DNN-HMM based system. For more contemporary DNN-HMM based ASR systems we investigated how adding different types of accented data to the training set can provide better recognition accuracy on accented speech. Finally, we proposed a new approach for visualisation of the AID feature space. This is helpful in analysing the AID recognition accuracies and analysing AID confusion matrices

Speech technologies for the audiovisual and multimedia interaction environments

361 p

The Effect Of Acoustic Variability On Automatic Speaker Recognition Systems

This thesis examines the influence of acoustic variability on automatic speaker recognition systems (ASRs) with three aims. i. To measure ASR performance under 5 commonly encountered acoustic conditions; ii. To contribute towards ASR system development with the provision of new research data; iii. To assess ASR suitability for forensic speaker comparison (FSC) application and investigative/pre-forensic use. The thesis begins with a literature review and explanation of relevant technical terms. Five categories of research experiments then examine ASR performance, reflective of conditions influencing speech quantity (inhibitors) and speech quality (contaminants), acknowledging quality often influences quantity. Experiments pertain to: net speech duration, signal to noise ratio (SNR), reverberation, frequency bandwidth and transcoding (codecs). The ASR system is placed under scrutiny with examination of settings and optimum conditions (e.g. matched/unmatched test audio and speaker models). Output is examined in relation to baseline performance and metrics assist in informing if ASRs should be applied to suboptimal audio recordings. Results indicate that modern ASRs are relatively resilient to low and moderate levels of the acoustic contaminants and inhibitors examined, whilst remaining sensitive to higher levels. The thesis provides discussion on issues such as the complexity and fragility of the speech signal path, speaker variability, difficulty in measuring conditions and mitigation (thresholds and settings). The application of ASRs to casework is discussed with recommendations, acknowledging the different modes of operation (e.g. investigative usage) and current UK limitations regarding presenting ASR output as evidence in criminal trials. In summary, and in the context of acoustic variability, the thesis recommends that ASRs could be applied to pre-forensic cases, accepting extraneous issues endure which require governance such as validation of method (ASR standardisation) and population data selection. However, ASRs remain unsuitable for broad forensic application with many acoustic conditions causing irrecoverable speech data loss contributing to high error rates

Exploring variabilities through factor analysis in automatic acoustic language recognition

La problématique traitée par la Reconnaissance de la Langue (LR) porte sur la définition découverte de la langue contenue dans un segment de parole. Cette thèse se base sur des paramètres acoustiques de courte durée, utilisés dans une approche d adaptation de mélanges de Gaussiennes (GMM-UBM). Le problème majeur de nombreuses applications du vaste domaine de la re- problème connaissance de formes consiste en la variabilité des données observées. Dans le contexte de la Reconnaissance de la Langue (LR), cette variabilité nuisible est due à des causes diverses, notamment les caractéristiques du locuteur, l évolution de la parole et de la voix, ainsi que les canaux d acquisition et de transmission. Dans le contexte de la reconnaissance du locuteur, l impact de la variabilité solution peut sensiblement être réduit par la technique d Analyse Factorielle (Joint Factor Analysis, JFA). Dans ce travail, nous introduisons ce paradigme à la Reconnaissance de la Langue. Le succès de la JFA repose sur plusieurs hypothèses. La première est que l information observée est décomposable en une partie universelle, une partie dépendante de la langue et une partie de variabilité, qui elle est indépendante de la langue. La deuxième hypothèse, plus technique, est que la variabilité nuisible se situe dans un sous-espace de faible dimension, qui est défini de manière globale.Dans ce travail, nous analysons le comportement de la JFA dans le contexte d un dispositif de LR du type GMM-UBM. Nous introduisons et analysons également sa combinaison avec des Machines à Vecteurs Support (SVM). Les premières publications sur la JFA regroupaient toute information qui est amélioration nuisible à la tâche (donc ladite variabilité) dans un seul composant. Celui-ci est supposé suivre une distribution Gaussienne. Cette approche permet de traiter les différentes sortes de variabilités d une manière unique. En pratique, nous observons que cette hypothèse n est pas toujours vérifiée. Nous avons, par exemple, le cas où les données peuvent être groupées de manière logique en deux sous-parties clairement distinctes, notamment en données de sources téléphoniques et d émissions radio. Dans ce cas-ci, nos recherches détaillées montrent un certain avantage à traiter les deux types de données par deux systèmes spécifiques et d élire comme score de sortie celui du système qui correspond à la catégorie source du segment testé. Afin de sélectionner le score de l un des systèmes, nous avons besoin d un analyses détecteur de canal source. Nous proposons ici différents nouveaux designs pour engendrées de tels détecteurs automatiques. Dans ce cadre, nous montrons que les facteurs de variabilité (du sous-espace) de la JFA peuvent être utilisés avec succès pour la détection de la source. Ceci ouvre la perspective intéressante de subdiviser les5données en catégories de canal source qui sont établies de manière automatique. En plus de pouvoir s adapter à des nouvelles conditions de source, cette propriété permettrait de pouvoir travailler avec des données d entraînement qui ne sont pas accompagnées d étiquettes sur le canal de source. L approche JFA permet une réduction de la mesure de coûts allant jusqu à généraux 72% relatives, comparé au système GMM-UBM de base. En utilisant des systèmes spécifiques à la source, suivis d un sélecteur de scores, nous obtenons une amélioration relative de 81%.Language Recognition is the problem of discovering the language of a spoken definitionutterance. This thesis achieves this goal by using short term acoustic information within a GMM-UBM approach.The main problem of many pattern recognition applications is the variability of problemthe observed data. In the context of Language Recognition (LR), this troublesomevariability is due to the speaker characteristics, speech evolution, acquisition and transmission channels.In the context of Speaker Recognition, the variability problem is solved by solutionthe Joint Factor Analysis (JFA) technique. Here, we introduce this paradigm toLanguage Recognition. The success of JFA relies on several assumptions: The globalJFA assumption is that the observed information can be decomposed into a universalglobal part, a language-dependent part and the language-independent variabilitypart. The second, more technical assumption consists in the unwanted variability part to be thought to live in a low-dimensional, globally defined subspace. In this work, we analyze how JFA behaves in the context of a GMM-UBM LR framework. We also introduce and analyze its combination with Support Vector Machines(SVMs).The first JFA publications put all unwanted information (hence the variability) improvemen tinto one and the same component, which is thought to follow a Gaussian distribution.This handles diverse kinds of variability in a unique manner. But in practice,we observe that this hypothesis is not always verified. We have for example thecase, where the data can be divided into two clearly separate subsets, namely datafrom telephony and from broadcast sources. In this case, our detailed investigations show that there is some benefit of handling the two kinds of data with two separatesystems and then to elect the output score of the system, which corresponds to the source of the testing utterance.For selecting the score of one or the other system, we need a channel source related analyses detector. We propose here different novel designs for such automatic detectors.In this framework, we show that JFA s variability factors (of the subspace) can beused with success for detecting the source. This opens the interesting perspectiveof partitioning the data into automatically determined channel source categories,avoiding the need of source-labeled training data, which is not always available.The JFA approach results in up to 72% relative cost reduction, compared to the overall resultsGMM-UBM baseline system. Using source specific systems followed by a scoreselector, we achieve 81% relative improvement.AVIGNON-Bib. numérique (840079901) / SudocSudocFranceF

Voice Modeling Methods for Automatic Speaker Recognition

Building a voice model means to capture the characteristics of a speaker´s voice in a data structure. This data structure is then used by a computer for further processing, such as comparison with other voices. Voice modeling is a vital step in the process of automatic speaker recognition that itself is the foundation of several applied technologies: (a) biometric authentication, (b) speech recognition and (c) multimedia indexing. Several challenges arise in the context of automatic speaker recognition. First, there is the problem of data shortage, i.e., the unavailability of sufficiently long utterances for speaker recognition. It stems from the fact that the speech signal conveys different aspects of the sound in a single, one-dimensional time series: linguistic (what is said?), prosodic (how is it said?), individual (who said it?), locational (where is the speaker?) and emotional features of the speech sound itself (to name a few) are contained in the speech signal, as well as acoustic background information. To analyze a specific aspect of the sound regardless of the other aspects, analysis methods have to be applied to a specific time scale (length) of the signal in which this aspect stands out of the rest. For example, linguistic information (i.e., which phone or syllable has been uttered?) is found in very short time spans of only milliseconds of length. On the contrary, speakerspecific information emerges the better the longer the analyzed sound is. Long utterances, however, are not always available for analysis. Second, the speech signal is easily corrupted by background sound sources (noise, such as music or sound effects). Their characteristics tend to dominate a voice model, if present, such that model comparison might then be mainly due to background features instead of speaker characteristics. Current automatic speaker recognition works well under relatively constrained circumstances, such as studio recordings, or when prior knowledge on the number and identity of occurring speakers is available. Under more adverse conditions, such as in feature films or amateur material on the web, the achieved speaker recognition scores drop below a rate that is acceptable for an end user or for further processing. For example, the typical speaker turn duration of only one second and the sound effect background in cinematic movies render most current automatic analysis techniques useless. In this thesis, methods for voice modeling that are robust with respect to short utterances and background noise are presented. The aim is to facilitate movie analysis with respect to occurring speakers. Therefore, algorithmic improvements are suggested that (a) improve the modeling of very short utterances, (b) facilitate voice model building even in the case of severe background noise and (c) allow for efficient voice model comparison to support the indexing of large multimedia archives. The proposed methods improve the state of the art in terms of recognition rate and computational efficiency. Going beyond selective algorithmic improvements, subsequent chapters also investigate the question of what is lacking in principle in current voice modeling methods. By reporting on a study with human probands, it is shown that the exclusion of time coherence information from a voice model induces an artificial upper bound on the recognition accuracy of automatic analysis methods. A proof-of-concept implementation confirms the usefulness of exploiting this kind of information by halving the error rate. This result questions the general speaker modeling paradigm of the last two decades and presents a promising new way. The approach taken to arrive at the previous results is based on a novel methodology of algorithm design and development called “eidetic design". It uses a human-in-the-loop technique that analyses existing algorithms in terms of their abstract intermediate results. The aim is to detect flaws or failures in them intuitively and to suggest solutions. The intermediate results often consist of large matrices of numbers whose meaning is not clear to a human observer. Therefore, the core of the approach is to transform them to a suitable domain of perception (such as, e.g., the auditory domain of speech sounds in case of speech feature vectors) where their content, meaning and flaws are intuitively clear to the human designer. This methodology is formalized, and the corresponding workflow is explicated by several use cases. Finally, the use of the proposed methods in video analysis and retrieval are presented. This shows the applicability of the developed methods and the companying software library sclib by means of improved results using a multimodal analysis approach. The sclib´s source code is available to the public upon request to the author. A summary of the contributions together with an outlook to short- and long-term future work concludes this thesis

A speaker classification framework for non-intrusive user modeling : speech-based personalization of in-car services

Speaker Classification, i.e. the automatic detection of certain characteristics of a person based on his or her voice, has a variety of applications in modern computer technology and artificial intelligence: As a non-intrusive source for user modeling, it can be employed for personalization of human-machine interfaces in numerous domains. This dissertation presents a principled approach to the design of a novel Speaker Classification system for automatic age and gender recognition which meets these demands. Based on literature studies, methods and concepts dealing with the underlying pattern recognition task are developed. The final system consists of an incremental GMM-SVM supervector architecture with several optimizations. An extensive data-driven experiment series explores the parameter space and serves as evaluation of the component. Further experiments investigate the language-independence of the approach. As an essential part of this thesis, a framework is developed that implements all tasks associated with the design and evaluation of Speaker Classification in an integrated development environment that is able to generate efficient runtime modules for multiple platforms. Applications from the automotive field and other domains demonstrate the practical benefit of the technology for personalization, e.g. by increasing local danger warning lead time for elderly drivers.Die Sprecherklassifikation, also die automatische Erkennung bestimmter Merkmale einer Person anhand ihrer Stimme, besitzt eine Vielzahl von Anwendungsmöglichkeiten in der modernen Computertechnik und Künstlichen Intelligenz: Als nicht-intrusive Wissensquelle für die Benutzermodellierung kann sie zur Personalisierung in vielen Bereichen eingesetzt werden. In dieser Dissertation wird ein fundierter Ansatz zum Entwurf eines neuartigen Sprecherklassifikationssystems zur automatischen Bestimmung von Alter und Geschlecht vorgestellt, welches diese Anforderungen erfüllt. Ausgehend von Literaturstudien werden Konzepte und Methoden zur Behandlung des zugrunde liegenden Mustererkennungsproblems entwickelt, welche zu einer inkrementell arbeitenden GMM-SVM-Supervector-Architektur mit diversen Optimierungen führen. Eine umfassende datengetriebene Experimentalreihe dient der Erforschung des Parameterraumes und zur Evaluierung der Komponente. Weitere Studien untersuchen die Sprachunabhängigkeit des Ansatzes. Als wesentlicher Bestandteil der Arbeit wird ein Framework entwickelt, das alle im Zusammenhang mit Entwurf und Evaluierung von Sprecherklassifikation anfallenden Aufgaben in einer integrierten Entwicklungsumgebung implementiert, welche effiziente Laufzeitmodule für verschiedene Plattformen erzeugen kann. Anwendungen aus dem Automobilbereich und weiteren Domänen demonstrieren den praktischen Nutzen der Technologie zur Personalisierung, z.B. indem die Vorlaufzeit von lokalen Gefahrenwarnungen für ältere Fahrer erhöht wird