Show from Tell: Audio-Visual Modelling in Clinical Settings

Auditory and visual signals usually present together and correlate with each other, not only in natural environments but also in clinical settings. However, the audio-visual modelling in the latter case can be more challenging, due to the different sources of audio/video signals and the noise (both signal-level and semantic-level) in auditory signals -- usually speech. In this paper, we consider audio-visual modelling in a clinical setting, providing a solution to learn medical representations that benefit various clinical tasks, without human expert annotation. A simple yet effective multi-modal self-supervised learning framework is proposed for this purpose. The proposed approach is able to localise anatomical regions of interest during ultrasound imaging, with only speech audio as a reference. Experimental evaluations on a large-scale clinical multi-modal ultrasound video dataset show that the proposed self-supervised method learns good transferable anatomical representations that boost the performance of automated downstream clinical tasks, even outperforming fully-supervised solutions

Features of hearing: applications of machine learning to uncover the building blocks of hearing

Recent advances in machine learning have instigated a renewed interest in using machine learning approaches to better understand human sensory processing. This line of research is particularly interesting for speech research since speech comprehension is uniquely human, which complicates obtaining detailed neural recordings. In this thesis, I explore how machine learning can be used to uncover new knowledge about the auditory system, with a focus on discovering robust auditory features. The resulting increased understanding of the noise robustness of human hearing may help to better assist those with hearing loss and improve Automatic Speech Recognition (ASR) systems. First, I show how computational neuroscience and machine learning can be combined to generate hypotheses about auditory features. I introduce a neural feature detection model with a modest number of parameters that is compatible with auditory physiology. By testing feature detector variants in a speech classification task, I confirm the importance of both well-studied and lesser-known auditory features. Second, I investigate whether ASR software is a good candidate model of the human auditory system. By comparing several state-of-the-art ASR systems to the results from humans on a range of psychometric experiments, I show that these ASR systems diverge markedly from humans in at least some psychometric tests. This implies that none of these systems act as a strong proxy for human speech recognition, although some may be useful when asking more narrowly defined questions. For neuroscientists, this thesis exemplifies how machine learning can be used to generate new hypotheses about human hearing, while also highlighting the caveats of investigating systems that may work fundamentally differently from the human brain. For machine learning engineers, I point to tangible directions for improving ASR systems. To motivate the continued cross-fertilization between these fields, a toolbox that allows researchers to assess new ASR systems has been released.Open Acces

Multimodal Computational Attention for Scene Understanding

Robotic systems have limited computational capacities. Hence, computational attention models are important to focus on specific stimuli and allow for complex cognitive processing. For this purpose, we developed auditory and visual attention models that enable robotic platforms to efficiently explore and analyze natural scenes. To allow for attention guidance in human-robot interaction, we use machine learning to integrate the influence of verbal and non-verbal social signals into our models

On combining acoustic and modulation spectrograms in an attention LSTM-based system for speech intelligibility level classification

Speech intelligibility can be affected by multiple factors, such as noisy environments, channel distortions or physiological issues. In this work, we deal with the problem of automatic prediction of the speech intelligibility level in this latter case. Starting from our previous work, a non-intrusive system based on LSTM networks with attention mechanism designed for this task, we present two main contributions. In the first one, it is proposed the use of per-frame modulation spectrograms as input features, instead of compact representations derived from them that discard important temporal information. In the second one, two different strategies for the combination of per-frame acoustic log-mel and modulation spectrograms into the LSTM framework are explored: at decision level or late fusion and at utterance level or Weighted-Pooling (WP) fusion. The proposed models are evaluated with the UA-Speech database that contains dysarthric speech with different degrees of severity. On the one hand, results show that attentional LSTM networks are able to adequately modeling the modulation spectrograms sequences producing similar classification rates as in the case of log-mel spectrograms. On the other hand, both combination strategies, late and WP fusion, outperform the single-feature systems, suggesting that per-frame log-mel and modulation spectrograms carry complementary information for the task of speech intelligibility prediction, than can be effectively exploited by the LSTM-based architectures, being the system with the WP fusion strategy and Attention-Pooling the one that achieves best results.The work leading to these results has been partly supported by the Spanish Government-MinECo under Projects TEC2017-84395-P and TEC2017-84593-C2-1-R.Publicad

Identifying, Evaluating and Applying Importance Maps for Speech

Like many machine learning systems, speech models often perform well when employed on data in the same domain as their training data. However, when the inference is on out-of-domain data, performance suffers. With a fast-growing number of applications of speech models in healthcare, education, automotive, automation, etc., it is essential to ensure that speech models can generalize to out-of-domain data, especially to noisy environments in real-world scenarios. In contrast, human listeners are quite robust to noisy environments. Thus, a thorough understanding of the differences between human listeners and speech models is urgently required to enhance speech model performance in noise. These differences exist presumably because the speech model does not use the same information as humans for recognizing the speech. A possible solution is encouraging the speech model to attend to the same time-frequency regions as human listeners. In this way, speech model generalization in noise may be improved. We define those time-frequency regions that humans or machines focus on to recognize the speech as importance maps (IMs). In this research, first, we investigate how to identify speech importance maps. Second, we compare human and machine importance maps to understand how they differ and how the speech model can learn from humans to improve its performance in noise. Third, we develop a structured saliency benchmark (SSBM), a metric for evaluating IMs. Finally, we propose a new application of IMs as data augmentation for speech models, enhancing their performance and enabling them to better generalize to out-of-domain noise. Overall, our work demonstrates that we can improve speech models and achieve out-of-domain generalization to different noise environments with importance maps. In the future, we will expand our work with large-scale speech models and deploy different methods to identify IMs and use them to augment the speech data, such as those based on human responses. We can also extend the technique to computer vision tasks, such as image recognition by predicting importance maps for images and use IMs to enhance model performance to out-of-domain data