63 research outputs found
Improving diagnostic procedures for epilepsy through automated recording and analysis of patients’ history
Transient loss of consciousness (TLOC) is a time-limited state of profound cognitive impairment characterised by amnesia, abnormal motor control, loss of responsiveness, a short duration and complete recovery. Most instances of TLOC are caused by one of three health conditions: epilepsy, functional (dissociative) seizures (FDS), or syncope. There is often a delay before the correct diagnosis is made and 10-20% of individuals initially receive an incorrect diagnosis. Clinical decision tools based on the endorsement of TLOC symptom lists have been limited to distinguishing between two causes of TLOC. The Initial Paroxysmal Event Profile (iPEP) has shown promise but was demonstrated to have greater accuracy in distinguishing between syncope and epilepsy or FDS than between epilepsy and FDS. The objective of this thesis was to investigate whether interactional, linguistic, and communicative differences in how people with epilepsy and people with FDS describe their experiences of TLOC can improve the predictive performance of the iPEP. An online web application was designed that collected information about TLOC symptoms and medical history from patients and witnesses using a binary questionnaire and verbal interaction with a virtual agent. We explored potential methods of automatically detecting these communicative differences, whether the differences were present during an interaction with a VA, to what extent these automatically detectable communicative differences improve the performance of the iPEP, and the acceptability of the application from the perspective of patients and witnesses. The two feature sets that were applied to previous doctor-patient interactions, features designed to measure formulation effort or detect semantic differences between the two groups, were able to predict the diagnosis with an accuracy of 71% and 81%, respectively. Individuals with epilepsy or FDS provided descriptions of TLOC to the VA that were qualitatively like those observed in previous research. Both feature sets were effective predictors of the diagnosis when applied to the web application recordings (85.7% and 85.7%). Overall, the accuracy of machine learning models trained for the threeway classification between epilepsy, FDS, and syncope using the iPEP responses from patients that were collected through the web application was worse than the performance observed in previous research (65.8% vs 78.3%), but the performance was increased by the inclusion of features extracted from the spoken descriptions on TLOC (85.5%). Finally, most participants who provided feedback reported that the online application was acceptable. These findings suggest that it is feasible to differentiate between people with epilepsy and people with FDS using an automated analysis of spoken seizure descriptions. Furthermore, incorporating these features into a clinical decision tool for TLOC can improve the predictive performance by improving the differential diagnosis between these two health conditions. Future research should use the feedback to improve the design of the application and increase perceived acceptability of the approach
End-to-end Lip-reading: A Preliminary Study
Deep lip-reading is the combination of the domains of computer vision and natural language processing. It uses deep neural networks to extract speech from silent videos. Most works in lip-reading use a multi staged training approach due to the complex nature of the task. A single stage, end-to-end, unified training approach, which is an ideal of machine learning, is also the goal in lip-reading. However, pure end-to-end systems have not yet been able to perform as good as non-end-to-end systems. Some exceptions to this are the very recent Temporal Convolutional Network (TCN) based architectures. This work lays out preliminary study of deep lip-reading, with a special focus on various end-to-end approaches. The research aims to test whether a purely end-to-end approach is justifiable for a task as complex as deep lip-reading. To achieve this, the meaning of pure end-to-end is first defined and several lip-reading systems that follow the definition are analysed. The system that most closely matches the definition is then adapted for pure end-to-end experiments. Four main contributions have been made: i) An analysis of 9 different end-to-end deep lip-reading systems, ii) Creation and public release of a pipeline1 to adapt sentence level Lipreading Sentences in the Wild 3 (LRS3) dataset into word level, iii) Pure end-to-end training of a TCN based network and evaluation on LRS3 word-level dataset as a proof of concept, iv) a public online portal2 to analyse visemes and experiment live end-to-end lip-reading inference. The study is able to verify that pure end-to-end is a sensible approach and an achievable goal for deep machine lip-reading
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Brain signal recognition using deep learning
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityBrain Computer Interface (BCI) has the potential to offer a new generation of applications independent of
muscular activity and controlled by the human brain. Brain imaging technologies are used to transfer the
cognitive tasks into control commands for a BCI system. The electroencephalography (EEG) technology
serves as the best available non-invasive solution for extracting signals from the brain. On the other hand,
speech is the primary means of communication, but for patients suffering from locked-in syndrome, there
is no easy way to communicate. Therefore, an ideal communication system for locked-in patients is a
thought-to-speech BCI system.
This research aims to investigate methods for the recognition of imagined speech from EEG signals
using deep learning techniques. In order to design an optimal imagined speech recognition BCI, variety
of issues have been solved. These include 1) proposing new feature extraction and classification
framework for recognition of imagined speech from EEG signals, 2) grammatical class recognition of
imagined words from EEG signals, 3) discriminating different cognitive tasks associated with speech in
the brain such as overt speech, covert speech, and visual imagery. In this work machine learning, deep
learning methods were used to analyze EEG signals.
For recognition of imagined speech from EEG signals, a new EEG database was collected while the
participants mentally spoke (imagined speech) the presented words. Along with imagined speech, EEG
data was recorded for visual imagery (imagining a scene or an image) and overt speech (verbal speech).
Spectro-temporal and spatio-temporal domain features were investigated for the classification of imagined
words from EEG signals. Further, a deep learning framework using the convolutional network
and attention mechanism was implemented for learning features in the spatial, temporal, and spectral
domains. The method achieved a recognition rate of 76.6% for three binary word pairs. These experiments
show that deep learning algorithms are ideal for imagined speech recognition from EEG signals
due to their ability to interpret features from non-linear and non-stationary signals. Grammatical classes
of imagined words from EEG signals were also recognized using a multi-channel convolution network
framework. This method was extended to a multi-level recognition system for multi-class classification
of imagined words which achieved an accuracy of 52.9% for 10 words, which is much better in
comparison to previous work.
In order to investigate the difference between imagined speech with verbal speech and visual imagery
from EEG signals, we used multivariate pattern analysis (MVPA). MVPA provided the time segments
when the neural oscillation for the different cognitive tasks was linearly separable. Further, frequencies
that result in most discrimination between the different cognitive tasks were also explored. A framework
was proposed to discriminate two cognitive tasks based on the spatio-temporal patterns in EEG signals.
The proposed method used the K-means clustering algorithm to find the best electrode combination and
convolutional-attention network for feature extraction and classification. The proposed method achieved
a high recognition rate of 82.9% and 77.7%.
The results in this research suggest that a communication based BCI system can be designed using
deep learning methods. Further, this work add knowledge to the existing work in the field of communication
based BCI system
Articulatory Copy Synthesis Based on the Speech Synthesizer VocalTractLab
Articulatory copy synthesis (ACS), a subarea of speech inversion, refers to the reproduction of natural utterances and involves both the physiological articulatory processes and their corresponding acoustic results. This thesis proposes two novel methods for the ACS of human speech using the articulatory speech synthesizer VocalTractLab (VTL) to address or mitigate the existing problems of speech inversion, such as non-unique mapping, acoustic variation among different speakers, and the time-consuming nature of the process.
The first method involved finding appropriate VTL gestural scores for given natural utterances using a genetic algorithm. It consisted of two steps: gestural score initialization and optimization. In the first step, gestural scores were initialized using the given acoustic signals with speech recognition, grapheme-to-phoneme (G2P), and a VTL rule-based method for converting phoneme sequences to gestural scores. In the second step, the initial gestural scores were optimized by a genetic algorithm via an analysis-by-synthesis (ABS) procedure that sought to minimize the cosine distance between the acoustic features of the synthetic and natural utterances. The articulatory parameters were also regularized during the optimization process to restrict them to reasonable values.
The second method was based on long short-term memory (LSTM) and convolutional neural networks, which were responsible for capturing the temporal dependence and the spatial structure of the acoustic features, respectively. The neural network regression models were trained, which used acoustic features as inputs and produced articulatory trajectories as outputs. In addition, to cover as much of the articulatory and acoustic space as possible, the training samples were augmented by manipulating the phonation type, speaking effort, and the vocal tract length of the synthetic utterances. Furthermore, two regularization methods were proposed: one based on the smoothness loss of articulatory trajectories and another based on the acoustic loss between original and predicted acoustic features.
The best-performing genetic algorithms and convolutional LSTM systems (evaluated in terms of the difference between the estimated and reference VTL articulatory parameters) obtained average correlation coefficients of 0.985 and 0.983 for speaker-dependent utterances, respectively, and their reproduced speech achieved recognition accuracies of 86.25% and 64.69% for speaker-independent utterances of German words, respectively. When applied to German sentence utterances, as well as English and Mandarin Chinese word utterances, the neural network based ACS systems achieved recognition accuracies of 73.88%, 52.92%, and 52.41%, respectively. The results showed that both of these methods not only reproduced the articulatory processes but also reproduced the acoustic signals of reference utterances. Moreover, the regularization methods led to more physiologically plausible articulatory processes and made the estimated articulatory trajectories be more articulatorily preferred by VTL, thus reproducing more natural and intelligible speech. This study also found that the convolutional layers, when used in conjunction with batch normalization layers, automatically learned more distinctive features from log power spectrograms. Furthermore, the neural network based ACS systems trained using German data could be generalized to the utterances of other languages
Reconhecimento de expressões faciais na lĂngua de sinais brasileira por meio do sistema de cĂłdigos de ação facial
Orientadores: Paula Dornhofer Paro Costa, Kate Mamhy Oliveira KumadaTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia ElĂ©trica e de ComputaçãoResumo: Surdos ao redor do mundo usam a lĂngua de sinais para se comunicarem, porĂ©m, apesar da ampla disseminação dessas lĂnguas, os surdos ou indivĂduos com deficiĂŞncia auditiva ainda enfrentam dificuldades na comunicação com ouvintes, na ausĂŞncia de um intĂ©rprete. Tais dificuldades impactam negativamente o acesso dos surdos Ă educação, ao mercado de trabalho e aos serviços pĂşblicos em geral. As tecnologias assistivas, como o Reconhecimento Automático de LĂngua de Sinais, do inglĂŞs Automatic Sign Language Recognition (ASLR), visam superar esses obstáculos de comunicação. No entanto, o desenvolvimento de sistemas ASLR confiáveis apresenta vários desafios devido Ă complexidade linguĂstica das lĂnguas de sinais. As lĂnguas de sinais (LSs) sĂŁo sistemas linguĂsticos visuoespaciais que, como qualquer outra lĂngua humana, apresentam variações linguĂsticas globais e regionais, alĂ©m de um sistema gramatical. AlĂ©m disso, as lĂnguas de sinais nĂŁo se baseiam apenas em gestos manuais, mas tambĂ©m em marcadores nĂŁo-manuais, como expressões faciais. Nas lĂnguas de sinais, as expressões faciais podem diferenciar itens lexicais, participar da construção sintática e contribuir para processos de intensificação, entre outras funções gramaticais e afetivas. Associado aos modelos de reconhecimento de gestos, o reconhecimento da expressões faciais Ă© um componente essencial da tecnologia ASLR. Neste trabalho, propomos um sistema automático de reconhecimento de expressões faciais para Libras, a lĂngua brasileira de sinais. A partir de uma pesquisa bibliográfica, apresentamos um estudo da linguagem e uma taxonomia diferente para expressões faciais de Libras associadas ao sistema de codificação de ações faciais. AlĂ©m disso, um conjunto de dados de expressões faciais em Libras foi criado. Com base em experimentos, a decisĂŁo sobre a construção do nosso sistema foi atravĂ©s de prĂ©-processamento e modelos de reconhecimento. Os recursos obtidos para a classificação das ações faciais sĂŁo resultado da aplicação combinada de uma regiĂŁo de interesse, e informações geomĂ©tricas da face dado embasamento teĂłrico e a obtenção de desempenho melhor do que outras etapas testadas. Quanto aos classificadores, o SqueezeNet apresentou melhores taxas de precisĂŁo. Com isso, o potencial do modelo proposto vem da análise de 77% da acurácia mĂ©dia de reconhecimento das expressões faciais de Libras. Este trabalho contribui para o crescimento dos estudos que envolvem a visĂŁo computacional e os aspectos de reconhecimento da estrutura das expressões faciais da lĂngua de sinais, e tem como foco principal a importância da anotação da ação facial de forma automatizadaAbstract: Deaf people around the world use sign languages to communicate but, despite the wide dissemination of such languages, deaf or hard of hearing individuals still face difficulties in communicating with hearing individuals, in the absence of an interpreter. Such difficulties negatively impact the access of deaf individuals to education, to the job market, and to public services in general. Assistive technologies, such as Automatic Sign Language Recognition (ASLR), aim at overcoming such communication obstacles. However, the development of reliable ASLR systems imposes numerous challenges due the linguistic complexity of sign languages. Sign languages (SLs) are visuospatial linguistic systems that, like any other human language, present global and regional linguistic variations, and a grammatical system. Also, sign languages do not rely only on manual gestures but also non-manual markers, such as facial expressions. In SL, facial expressions may differentiate lexical items, participate in syntactic construction, and contribute to change the intensity of a sentence, among other grammatical and affective functions. Associated with the gesture recognition models, facial expression recognition (FER) is an essential component of ASLR technology. In this work, we propose an automatic facial expression recognition (FER) system for Brazilian Sign Language (Libras). Derived from a literature survey, we present a language study and a different taxonomy for facial expressions of Libras associated with the Facial Action Coding System (FACS). Also, a dataset of facial expressions in Libras was created. An experimental setting was done for the construction of our framework for a preprocessing stage and recognizer model. The features for the classification of the facial actions resulted from the application of a combined region of interest and geometric information given a theoretical basis and better performance than other tested steps. As for classifiers, SqueezeNet returned better accuracy rates. With this, the potential of the proposed model comes from the analysis of 77% of the average accuracy of recognition of Libras' facial expressions. This work contributes to the growth of studies that involve the computational vision and recognition aspects of the structure of sign language facial expressions, and its main focus is the importance of facial action annotation in an automated wayDoutoradoEngenharia de ComputaçãoDoutora em Engenharia ElĂ©trica001CAPE
An Ordinal Approach to Affective Computing
Both depression prediction and emotion recognition systems are often based on ordinal ground truth due to subjectively annotated datasets. Yet, both have so far been posed as classification or regression problems. These naive approaches have fundamental issues because they are not focused on ordering, unlike ordinal regression, which is the most appropriate for truly ordinal ground truth. Ordinal regression to date offers comparatively fewer, more limited methods when compared with other branches in machine learning, and its usage has been limited to specific research domains. Accordingly, this thesis presents investigations into ordinal approaches for affective computing by describing a consistent framework to understand all ordinal system designs, proposing ordinal systems for large datasets, and introducing tools and principles to select suitable system designs and evaluation methods.
First, three learning approaches are compared using the support vector framework to establish the empirical advantages of ordinal regression, which is lacking from the current literature. Results on depression and emotion corpora indicate that ordinal regression with proper tuning can improve existing depression and emotion systems. Ordinal logistic regression (OLR), which is an extension of logistic regression for ordinal scales, contributes to a number of model structures, from which the best structure must be chosen. Exploiting the newly proposed computationally efficient greedy algorithm for model structure selection (GREP), OLR outperformed or was comparable with state-of-the-art depression systems on two benchmark depression speech datasets.
Deep learning has dominated many affective computing fields, and hence ordinal deep learning is an attractive prospect. However, it is under-studied even in the machine learning literature, which motivates an in-depth analysis of appropriate network architectures and loss functions. One of the significant outcomes of this analysis is the introduction of RankCNet, a novel ordinal network which utilises a surrogate loss function of rank correlation.
Not only the modelling algorithm but the choice of evaluation measure depends on the nature of the ground truth. Rank correlation measures, which are sensitive to ordering, are more apt for ordinal problems than common classification or regression measures that ignore ordering information. Although rank-based evaluation for ordinal problems is not new, so far in affective computing, ordinality of the ground truth has been widely ignored during evaluation. Hence, a systematic analysis in the affective computing context is presented, to provide clarity and encourage careful choice of evaluation measures. Another contribution is a neural network framework with a novel multi-term loss function to assess the ordinality of ordinally-annotated datasets, which can guide the selection of suitable learning and evaluation methods. Experiments on multiple synthetic and affective speech datasets reveal that the proposed system can offer reliable and meaningful predictions about the ordinality of a given dataset.
Overall, the novel contributions and findings presented in this thesis not only improve prediction accuracy but also encourage future research towards ordinal affective computing: a different paradigm, but often the most appropriate
USING DEEP LEARNING-BASED FRAMEWORK FOR CHILD SPEECH EMOTION RECOGNITION
Biological languages of the body through which human emotion can be detected abound including heart rate, facial expressions, movement of the eyelids and dilation of the eyes, body postures, skin conductance, and even the speech we make. Speech emotion recognition research started some three decades ago, and the popular Interspeech Emotion Challenge has helped to propagate this research area. However, most speech recognition research is focused on adults and there is very little research on child speech. This dissertation is a description of the development and evaluation of a child speech emotion recognition framework. The higher-level components of the framework are designed to sort and separate speech based on the speaker’s age, ensuring that focus is only on speeches made by children. The framework uses Baddeley’s Theory of Working Memory to model a Working Memory Recurrent Network that can process and recognize emotions from speech. Baddeley’s Theory of Working Memory offers one of the best explanations on how the human brain holds and manipulates temporary information which is very crucial in the development of neural networks that learns effectively. Experiments were designed and performed to provide answers to the research questions, evaluate the proposed framework, and benchmark the performance of the framework with other methods. Satisfactory results were obtained from the experiments and in many cases, our framework was able to outperform other popular approaches. This study has implications for various applications of child speech emotion recognition such as child abuse detection and child learning robots
Speech Recognition
Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes
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