Temporal Attention-Gated Model for Robust Sequence Classification

Typical techniques for sequence classification are designed for well-segmented sequences which have been edited to remove noisy or irrelevant parts. Therefore, such methods cannot be easily applied on noisy sequences expected in real-world applications. In this paper, we present the Temporal Attention-Gated Model (TAGM) which integrates ideas from attention models and gated recurrent networks to better deal with noisy or unsegmented sequences. Specifically, we extend the concept of attention model to measure the relevance of each observation (time step) of a sequence. We then use a novel gated recurrent network to learn the hidden representation for the final prediction. An important advantage of our approach is interpretability since the temporal attention weights provide a meaningful value for the salience of each time step in the sequence. We demonstrate the merits of our TAGM approach, both for prediction accuracy and interpretability, on three different tasks: spoken digit recognition, text-based sentiment analysis and visual event recognition.Comment: Accepted by CVPR 201

Phonocardiographic sensing using deep learning for abnormal heartbeat detection

Deep learning-based cardiac auscultation is of significant interest to the healthcare community as it can help reducing the burden of manual auscultation with automated detection of abnormal heartbeats. However, the problem of automatic cardiac auscultation is complicated due to the requirement of reliable and highly accurate systems, which are robust to the background noise in the heartbeat sound. In this paper, we propose a Recurrent Neural Networks (RNNs)-based automated cardiac auscultation solution. Our choice of RNNs is motivated by their great success of modeling sequential or temporal data even in the presence of noise. We explore the use of various RNN models, and demonstrate that these models significantly outperform the best reported results in the literature. We also present the run-time complexity of various RNNs, which provides insight about their complexity versus performance trade-offs

Personalized face and gesture analysis using hierarchical neural networks

The video-based computational analyses of human face and gesture signals encompass a myriad of challenging research problems involving computer vision, machine learning and human computer interaction. In this thesis, we focus on the following challenges: a) the classification of hand and body gestures along with the temporal localization of their occurrence in a continuous stream, b) the recognition of facial expressivity levels in people with Parkinson's Disease using multimodal feature representations, c) the prediction of student learning outcomes in intelligent tutoring systems using affect signals, and d) the personalization of machine learning models, which can adapt to subject and group-specific nuances in facial and gestural behavior. Specifically, we first conduct a quantitative comparison of two approaches to the problem of segmenting and classifying gestures on two benchmark gesture datasets: a method that simultaneously segments and classifies gestures versus a cascaded method that performs the tasks sequentially. Second, we introduce a framework that computationally predicts an accurate score for facial expressivity and validate it on a dataset of interview videos of people with Parkinson's disease. Third, based on a unique dataset of videos of students interacting with MathSpring, an intelligent tutoring system, collected by our collaborative research team, we build models to predict learning outcomes from their facial affect signals. Finally, we propose a novel solution to a relatively unexplored area in automatic face and gesture analysis research: personalization of models to individuals and groups. We develop hierarchical Bayesian neural networks to overcome the challenges posed by group or subject-specific variations in face and gesture signals. We successfully validate our formulation on the problems of personalized subject-specific gesture classification, context-specific facial expressivity recognition and student-specific learning outcome prediction. We demonstrate the flexibility of our hierarchical framework by validating the utility of both fully connected and recurrent neural architectures

Symbol Emergence in Robotics: A Survey

Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment and other systems. Understanding human social interactions and developing a robot that can smoothly communicate with human users in the long term, requires an understanding of the dynamics of symbol systems and is crucially important. The embodied cognition and social interaction of participants gradually change a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER is a constructive approach towards an emergent symbol system. The emergent symbol system is socially self-organized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e., humans and developmental robots. Specifically, we describe some state-of-art research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain words and their embodied meanings from raw sensory--motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions of research in SER.Comment: submitted to Advanced Robotic

Voice Analysis for Stress Detection and Application in Virtual Reality to Improve Public Speaking in Real-time: A Review

Stress during public speaking is common and adversely affects performance and self-confidence. Extensive research has been carried out to develop various models to recognize emotional states. However, minimal research has been conducted to detect stress during public speaking in real time using voice analysis. In this context, the current review showed that the application of algorithms was not properly explored and helped identify the main obstacles in creating a suitable testing environment while accounting for current complexities and limitations. In this paper, we present our main idea and propose a stress detection computational algorithmic model that could be integrated into a Virtual Reality (VR) application to create an intelligent virtual audience for improving public speaking skills. The developed model, when integrated with VR, will be able to detect excessive stress in real time by analysing voice features correlated to physiological parameters indicative of stress and help users gradually control excessive stress and improve public speaking performanceComment: 41 pages, 7 figures, 4 table

Productivity Measurement of Call Centre Agents using a Multimodal Classification Approach

Call centre channels play a cornerstone role in business communications and transactions, especially in challenging business situations. Operations’ efficiency, service quality, and resource productivity are core aspects of call centres’ competitive advantage in rapid market competition. Performance evaluation in call centres is challenging due to human subjective evaluation, manual assortment to massive calls, and inequality in evaluations because of different raters. These challenges impact these operations' efficiency and lead to frustrated customers. This study aims to automate performance evaluation in call centres using various deep learning approaches. Calls recorded in a call centre are modelled and classified into high- or low-performance evaluations categorised as productive or nonproductive calls. The proposed conceptual model considers a deep learning network approach to model the recorded calls as text and speech. It is based on the following: 1) focus on the technical part of agent performance, 2) objective evaluation of the corpus, 3) extension of features for both text and speech, and 4) combination of the best accuracy from text and speech data using a multimodal structure. Accordingly, the diarisation algorithm extracts that part of the call where the agent is talking from which the customer is doing so. Manual annotation is also necessary to divide the modelling corpus into productive and nonproductive (supervised training). Krippendorff’s alpha was applied to avoid subjectivity in the manual annotation. Arabic speech recognition is then developed to transcribe the speech into text. The text features are the words embedded using the embedding layer. The speech features make several attempts to use the Mel Frequency Cepstral Coefficient (MFCC) upgraded with Low-Level Descriptors (LLD) to improve classification accuracy. The data modelling architectures for speech and text are based on CNNs, BiLSTMs, and the attention layer. The multimodal approach follows the generated models to improve performance accuracy by concatenating the text and speech models using the joint representation methodology. The main contributions of this thesis are: • Developing an Arabic Speech recognition method for automatic transcription of speech into text. • Drawing several DNN architectures to improve performance evaluation using speech features based on MFCC and LLD. • Developing a Max Weight Similarity (MWS) function to outperform the SoftMax function used in the attention layer. • Proposing a multimodal approach for combining the text and speech models for best performance evaluation

Expressive movement generation with machine learning

Movement is an essential aspect of our lives. Not only do we move to interact with our physical environment, but we also express ourselves and communicate with others through our movements. In an increasingly computerized world where various technologies and devices surround us, our movements are essential parts of our interaction with and consumption of computational devices and artifacts. In this context, incorporating an understanding of our movements within the design of the technologies surrounding us can significantly improve our daily experiences. This need has given rise to the field of movement computing – developing computational models of movement that can perceive, manipulate, and generate movements. In this thesis, we contribute to the field of movement computing by building machine-learning-based solutions for automatic movement generation. In particular, we focus on using machine learning techniques and motion capture data to create controllable, generative movement models. We also contribute to the field by creating datasets, tools, and libraries that we have developed during our research. We start our research by reviewing the works on building automatic movement generation systems using machine learning techniques and motion capture data. Our review covers background topics such as high-level movement characterization, training data, features representation, machine learning models, and evaluation methods. Building on our literature review, we present WalkNet, an interactive agent walking movement controller based on neural networks. The expressivity of virtual, animated agents plays an essential role in their believability. Therefore, WalkNet integrates controlling the expressive qualities of movement with the goal-oriented behaviour of an animated virtual agent. It allows us to control the generation based on the valence and arousal levels of affect, the movement’s walking direction, and the mover’s movement signature in real-time. Following WalkNet, we look at controlling movement generation using more complex stimuli such as music represented by audio signals (i.e., non-symbolic music). Music-driven dance generation involves a highly non-linear mapping between temporally dense stimuli (i.e., the audio signal) and movements, which renders a more challenging modelling movement problem. To this end, we present GrooveNet, a real-time machine learning model for music-driven dance generation