Speech emotion recognition through statistical classification

O propósito desta dissertação é a discussão do reconhecimento de emoção na voz. Para este fim, criou-se uma base de dados validada de discurso emocional simulado Português, intitulada European Portuguese Emotional Discourse Database (EPEDD) e foram operados algoritmos de classificação estatística nessa base de dados. EPEDD é uma base de dados simulada, caracterizada por pequenos discursos (5 frases longas, 5 frases curtas e duas palavras), todos eles pronunciados por 8 atores—ambos os sexos igualmente representados—em 9 diferentes emoções (raiva, alegria, nojo, excitação, apatia, medo, surpresa, tristeza e neutro), baseadas no modelo de emoções de Lövheim. Concretizou-se uma avaliação de 40% da base de dados por avaliadores inexperientes, filtrando 60% dos pequenos discursos, com o intuito de criar uma base de dados validada. A base de dados completa contem 718 instâncias, enquanto que a base de dados validada contém 116 instâncias. A qualidade média de representação teatral, numa escala de a 5 foi avaliada como 2,3. A base de dados validada é composta por discurso emocional cujas emoções são reconhecidas com uma taxa média de 69,6%, por avaliadores inexperientes. A raiva tem a taxa de reconhecimento mais elevada com 79,7%, enquanto que o nojo, a emoção cuja taxa de reconhecimento é a mais baixa, consta com 40,5%. A extração de características e a classificação estatística foi realizada respetivamente através dos softwares Opensmile e Weka. Os algoritmos foram operados na base dados original e na base de dados avaliada, tendo sido obtidos os melhores resultados através de SVMs, respetivamente com 48,7% e 44,0%. A apatia obteve a taxa de reconhecimento mais elevada com 79,0%, enquanto que a excitação obteve a taxa de reconhecimento mais baixa com 32,9%.The purpose of this dissertation is to discuss speech emotion recognition. It was created a validated acted Portuguese emotional speech database, named European Portuguese Emotional Discourse Database (EPEDD), and statistical classification algorithms have been applied on it. EPEDD is an acted database, featuring 12 utterances (2 single-words, 5 short sentences and 5 long sentences) per actor and per emotion, 8 actors, both genders equally represented, and 9 emotions (anger, joy, disgust, excitement, fear, apathy, surprise, sadness and neutral), based on Lövheim’s emotion model. We had 40% of the database evaluated by unexperienced evaluators, enabling us to produce a validated one, filtering 60% of the evaluated utterances. The full database contains 718 instances, while the validated one contains 116 instances. The average acting quality of the original database was evaluated, in a scale from 1 to 5, as 2,3. The validated database is composed by emotional utterances that have their emotions recognized on average at a 69,6% rate, by unexperienced judges. Anger had the highest recognition rate at 79,7%, while disgust had the lowest recognition rate at 40,5%. Feature extraction and statistical classification algorithms were performed respectively applying Opensmile and Weka software. Statistical classification algorithms operated in the full database and in the validated one, best results being obtained by SVMs, respectively the emotion recognition rates being 48,7% and 44,0%. Apathy had the highest recognition rate: 79.0%, while excitement had the lowest emotion recognition rate: 32.9%

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the newborn to the adult and elderly. Over the years the initial issues have grown and spread also in other fields of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years in Firenze, Italy. This edition celebrates twenty-two years of uninterrupted and successful research in the field of voice analysis

Prosodic and Voice Quality Cross-Language Analysis of Storytelling Expressive Categories Oriented to Text-To-Speech Synthesis

Durant segles, la interpretació oral de contes i històries ha sigut una tradició mundial lligada a l’entreteniment, la educació, i la perpetuació de la cultura. En les últimes dècades, alguns treballs s’han centrat en analitzar aquest estil de parla ric en matisos expressius caracteritzats per determinats patrons acústics. En relació a això, també hi ha hagut un interès creixent en desenvolupar aplicacions de contar contes, com ara les de contacontes interactius. Aquesta tesi està orientada a millorar aspectes claus d’aquest tipus d’aplicacions: millorar la naturalitat de la parla sintètica expressiva a partir d’analitzar la parla de contacontes en detall, a més a més de proporcionar un millor llenguatge no verbal a un avatar parlant mitjançant la sincronització de la parla i els gestos. Per aconseguir aquests objectius és necessari comprendre les característiques acústiques d’aquest estil de parla i la interacció de la parla i els gestos. Pel que fa a característiques acústiques de la parla de contacontes, la literatura relacionada ha treballat en termes de prosòdia, mentre que només ha estat suggerit que la qualitat de la veu pot jugar un paper important per modelar les subtileses d’aquest estil. En aquesta tesi, el paper tant de la prosòdia com de la qualitat de la veu en l’estil indirecte de la parla de contacontes en diferents idiomes és analitzat per identificar les principal categories expressives que la composen i els paràmetres acústics que les caracteritzen. Per fer-ho, es proposa una metodologia d’anotació per aquest estil de parla a nivell de oració basada en modes de discurs dels contes (mode narratiu, descriptiu, i diàleg), introduint a més sub-modes narratius. Considerant aquesta metodologia d’anotació, l’estil indirecte d’una història orientada a una audiència jove (cobrint versions en castellà, anglès, francès, i alemany) és analitzat en termes de prosòdia i qualitat de la veu mitjançant anàlisis estadístics i discriminants, després de classificar els àudios de les oracions de la història en les seves categories expressives. Els resultats confirmen l’existència de les categories de contes amb diferències expressives subtils en tots els idiomes més enllà dels estils personals dels narradors. En aquest sentit, es presenten evidències que suggereixen que les categories expressives dels contes es transmeten amb matisos expressius més subtils que en les emocions bàsiques, després de comparar els resultats obtinguts amb aquells de parla emocional. Els anàlisis també mostren que la prosòdia i la qualitat de la veu contribueixen pràcticament de la mateixa manera a l’hora de discriminar entre les categories expressives dels contes, les quals son expressades amb patrons acústics similars en tots els idiomes analitzats. Cal destacar també la gran relació observada en la selecció de categoria per cada oració que han fet servir els diferents narradors encara quan, que sapiguem, no se’ls hi va donar cap indicació. Per poder traslladar totes aquestes categories a un sistema de text a parla basat en corpus, caldria enregistrar un corpus per cada categoria. No obstant, crear diferents corpus ad-hoc esdevé un tasca molt laboriosa. En la tesi, s’introdueix una alternativa basada en una metodologia d’anàlisi orientada a síntesi dissenyada per derivar models de regles des de un petit però representatiu conjunt d’oracions, que poden poder ser utilitzats per generar parla amb estil de contacontes a partir de parla neutra. Els experiments sobre suspens creixent com a prova de concepte mostren la viabilitat de la proposta en termes de naturalitat i similitud respecte un narrador de contes real. Finalment, pel que fa a interacció entre parla i gestos, es realitza un anàlisi de sincronia i èmfasi orientat a controlar un avatar de contacontes en 3D. Al tal efecte, es defineixen indicadors de força tant per els gestos com per la parla. Després de validar-los amb tests perceptius, una regla d’intensitat s’obté de la seva correlació. A més a més, una regla de sincronia es deriva per determinar correspondències temporals entre els gestos i la parla. Aquests anàlisis s’han dut a terme sobre interpretacions neutres i agressives per part d’un actor per cobrir un gran rang de nivells d’èmfasi, com a primer pas per avaluar la integració d’un avatar parlant després del sistema de text a parla.Durante siglos, la interpretación oral de cuentos e historias ha sido una tradición mundial ligada al entretenimiento, la educación, y la perpetuación de la cultura. En las últimas décadas, algunos trabajos se han centrado en analizar este estilo de habla rico en matices expresivos caracterizados por determinados patrones acústicos. En relación a esto, también ha habido un interés creciente en desarrollar aplicaciones de contar cuentos, como las de cuentacuentos interactivos. Esta tesis está orientada a mejorar aspectos claves de este tipo de aplicaciones: mejorar la naturalidad del habla sintética expresiva a partir de analizar el habla de cuentacuentos en detalle, además de proporcionar un mejor lenguaje no verbal a un avatar parlante mediante la sincronización del habla y los gestos. Para conseguir estos objetivos es necesario comprender las características acústicas de este estilo de habla y la interacción del habla y los gestos. En cuanto a características acústicas del habla de narradores de cuentos, la literatura relacionada ha trabajado en términos de prosodia, mientras que sólo ha sido sugerido que la calidad de la voz puede jugar un papel importante para modelar las sutilezas de este estilo. En esta tesis, el papel tanto de la prosodia como de la calidad de la voz en el estilo indirecto del habla de cuentacuentos en diferentes idiomas es analizado para identificar las principales categorías expresivas que componen este estilo de habla y los parámetros acústicos que las caracterizan. Para ello, se propone una metodología de anotación a nivel de oración basada en modos de discurso de los cuentos (modo narrativo, descriptivo, y diálogo), introduciendo además sub-modos narrativos. Considerando esta metodología de anotación, el estilo indirecto de una historia orientada a una audiencia joven (cubriendo versiones en castellano, inglés, francés, y alemán) es analizado en términos de prosodia y calidad de la voz mediante análisis estadísticos y discriminantes, después de clasificar los audios de las oraciones de la historia en sus categorías expresivas. Los resultados confirman la existencia de las categorías de cuentos con diferencias expresivas sutiles en todos los idiomas más allá de los estilos personales de los narradores. En este sentido, se presentan evidencias que sugieren que las categorías expresivas de los cuentos se transmiten con matices expresivos más sutiles que en las emociones básicas, tras comparar los resultados obtenidos con aquellos de habla emocional. Los análisis también muestran que la prosodia y la calidad de la voz contribuyen prácticamente de la misma manera a la hora de discriminar entre las categorías expresivas de los cuentos, las cuales son expresadas con patrones acústicos similares en todos los idiomas analizados. Cabe destacar también la gran relación observada en la selección de categoría para cada oración que han utilizado los diferentes narradores aun cuando, que sepamos, no se les dio ninguna indicación. Para poder trasladar todas estas categorías a un sistema de texto a habla basado en corpus, habría que grabar un corpus para cada categoría. Sin embargo, crear diferentes corpus ad-hoc es una tarea muy laboriosa. En la tesis, se introduce una alternativa basada en una metodología de análisis orientada a síntesis diseñada para derivar modelos de reglas desde un pequeño pero representativo conjunto de oraciones, que pueden ser utilizados para generar habla de cuentacuentos a partir de neutra. Los experimentos sobre suspense creciente como prueba de concepto muestran la viabilidad de la propuesta en términos de naturalidad y similitud respecto a un narrador de cuentos real. Finalmente, en cuanto a interacción entre habla y gestos, se realiza un análisis de sincronía y énfasis orientado a controlar un avatar cuentacuentos en 3D. Al tal efecto, se definen indicadores de fuerza tanto para gestos como para habla. Después de validarlos con tests perceptivos, una regla de intensidad se obtiene de su correlación. Además, una regla de sincronía se deriva para determinar correspondencias temporales entre los gestos y el habla. Estos análisis se han llevado a cabo sobre interpretaciones neutras y agresivas por parte de un actor para cubrir un gran rango de niveles de énfasis, como primer paso para evaluar la integración de un avatar parlante después del sistema de texto a habla.For ages, the oral interpretation of tales and stories has been a worldwide tradition tied to entertainment, education, and perpetuation of culture. During the last decades, some works have focused on the analysis of this particular speaking style rich in subtle expressive nuances represented by specific acoustic cues. In line with this fact, there has also been a growing interest in the development of storytelling applications, such as those related to interactive storytelling. This thesis deals with one of the key aspects of audiovisual storytellers: improving the naturalness of the expressive synthetic speech by analysing the storytelling speech in detail, together with providing better non-verbal language to a speaking avatar by synchronizing that speech with its gestures. To that effect, it is necessary to understand in detail the acoustic characteristics of this particular speaking style and the interaction between speech and gestures. Regarding the acoustic characteristics of storytelling speech, the related literature has dealt with the acoustic analysis of storytelling speech in terms of prosody, being only suggested that voice quality may play an important role for the modelling of its subtleties. In this thesis, the role of both prosody and voice quality in indirect storytelling speech is analysed across languages to identify the main expressive categories it is composed of together with the acoustic parameters that characterize them. To do so, an analysis methodology is proposed to annotate this particular speaking style at the sentence level based on storytelling discourse modes (narrative, descriptive, and dialogue), besides introducing narrative sub-modes. Considering this annotation methodology, the indirect speech of a story oriented to a young audience (covering the Spanish, English, French, and German versions) is analysed in terms of prosody and voice quality through statistical and discriminant analyses, after classifying the sentence-level utterances of the story in their corresponding expressive categories. The results confirm the existence of storytelling categories containing subtle expressive nuances across the considered languages beyond narrators' personal styles. In this sense, evidences are presented suggesting that such storytelling expressive categories are conveyed with subtler speech nuances than basic emotions by comparing their acoustic patterns to the ones obtained from emotional speech data. The analyses also show that both prosody and voice quality contribute almost equally to the discrimination among storytelling expressive categories, being conveyed with similar acoustic patterns across languages. It is also worth noting the strong relationship observed in the selection of the expressive category per utterance across the narrators even when, up to our knowledge, no previous indications were given to them. In order to translate all these expressive categories to a corpus-based Text-To-Speech system, the recording of a speech corpus for each category would be required. However, building ad-hoc speech corpora for each and every specific expressive style becomes a very daunting task. In this work, we introduce an alternative based on an analysis-oriented-to-synthesis methodology designed to derive rule-based models from a small but representative set of utterances, which can be used to generate storytelling speech from neutral speech. The experiments conducted on increasing suspense as a proof of concept show the viability of the proposal in terms of naturalness and storytelling resemblance. Finally, in what concerns the interaction between speech and gestures, an analysis is performed in terms of time and emphasis oriented to drive a 3D storytelling avatar. To that effect, strength indicators are defined for speech and gestures. After validating them through perceptual tests, an intensity rule is obtained from their correlation. Moreover, a synchrony rule is derived to determine temporal correspondences between speech and gestures. These analyses have been conducted on aggressive and neutral performances to cover a broad range of emphatic levels as a first step to evaluate the integration of a speaking avatar after the expressive Text-To-Speech system

Image processing methods to segment speech spectrograms for word level recognition

The ultimate goal of automatic speech recognition (ASR) research is to allow a computer to recognize speech in real-time, with full accuracy, independent of vocabulary size, noise, speaker characteristics or accent. Today, systems are trained to learn an individual speaker's voice and larger vocabularies statistically, but accuracy is not ideal. A small gap between actual speech and acoustic speech representation in the statistical mapping causes a failure to produce a match of the acoustic speech signals by Hidden Markov Model (HMM) methods and consequently leads to classification errors. Certainly, these errors in the low level recognition stage of ASR produce unavoidable errors at the higher levels. Therefore, it seems that ASR additional research ideas to be incorporated within current speech recognition systems. This study seeks new perspective on speech recognition. It incorporates a new approach for speech recognition, supporting it with wider previous research, validating it with a lexicon of 533 words and integrating it with a current speech recognition method to overcome the existing limitations. The study focusses on applying image processing to speech spectrogram images (SSI). We, thus develop a new writing system, which we call the Speech-Image Recogniser Code (SIR-CODE). The SIR-CODE refers to the transposition of the speech signal to an artificial domain (the SSI) that allows the classification of the speech signal into segments. The SIR-CODE allows the matching of all speech features (formants, power spectrum, duration, cues of articulation places, etc.) in one process. This was made possible by adding a Realization Layer (RL) on top of the traditional speech recognition layer (based on HMM) to check all sequential phones of a word in single step matching process. The study shows that the method gives better recognition results than HMMs alone, leading to accurate and reliable ASR in noisy environments. Therefore, the addition of the RL for SSI matching is a highly promising solution to compensate for the failure of HMMs in low level recognition. In addition, the same concept of employing SSIs can be used for whole sentences to reduce classification errors in HMM based high level recognition. The SIR-CODE bridges the gap between theory and practice of phoneme recognition by matching the SSI patterns at the word level. Thus, it can be adapted for dynamic time warping on the SIR-CODE segments, which can help to achieve ASR, based on SSI matching alone

AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model

© 2020, The Author(s). The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods requires a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid, and it is unnecessary to execute them to find out whether they are good pipelines. To address this issue, we propose a novel method to evaluate the validity of ML pipelines using a surrogate model (AVATAR). The AVATAR enables to accelerate automatic ML pipeline composition and optimisation by quickly ignoring invalid pipelines. Our experiments show that the AVATAR is more efficient in evaluating complex pipelines in comparison with the traditional evaluation approaches requiring their execution

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen