Importance of Smoothness Induced by Optimizers in FL4ASR: Towards Understanding Federated Learning for End-to-End ASR

In this paper, we start by training End-to-End Automatic Speech Recognition (ASR) models using Federated Learning (FL) and examining the fundamental considerations that can be pivotal in minimizing the performance gap in terms of word error rate between models trained using FL versus their centralized counterpart. Specifically, we study the effect of (i) adaptive optimizers, (ii) loss characteristics via altering Connectionist Temporal Classification (CTC) weight, (iii) model initialization through seed start, (iv) carrying over modeling setup from experiences in centralized training to FL, e.g., pre-layer or post-layer normalization, and (v) FL-specific hyperparameters, such as number of local epochs, client sampling size, and learning rate scheduler, specifically for ASR under heterogeneous data distribution. We shed light on how some optimizers work better than others via inducing smoothness. We also summarize the applicability of algorithms, trends, and propose best practices from prior works in FL (in general) toward End-to-End ASR models.Comment: In Proceedings of the IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) 202

Towards Domain-Agnostic Contrastive Learning

Despite recent success, most contrastive self-supervised learning methods are domain-specific, relying heavily on data augmentation techniques that require knowledge about a particular domain, such as image cropping and rotation. To overcome such limitation, we propose a novel domain-agnostic approach to contrastive learning, named DACL, that is applicable to domains where invariances, and thus, data augmentation techniques, are not readily available. Key to our approach is the use of Mixup noise to create similar and dissimilar examples by mixing data samples differently either at the input or hidden-state levels. To demonstrate the effectiveness of DACL, we conduct experiments across various domains such as tabular data, images, and graphs. Our results show that DACL not only outperforms other domain-agnostic noising methods, such as Gaussian-noise, but also combines well with domain-specific methods, such as SimCLR, to improve self-supervised visual representation learning. Finally, we theoretically analyze our method and show advantages over the Gaussian-noise based contrastive learning approach.Comment: Published in ICML 202

Deep audio-visual speech recognition

Decades of research in acoustic speech recognition have led to systems that we use in our everyday life. However, even the most advanced speech recognition systems fail in the presence of noise. The degraded performance can be compensated by introducing visual speech information. However, Visual Speech Recognition (VSR) in naturalistic conditions is very challenging, in part due to the lack of architectures and annotations. This thesis contributes towards the problem of Audio-Visual Speech Recognition (AVSR) from different aspects. Firstly, we develop AVSR models for isolated words. In contrast to previous state-of-the-art methods that consists of a two-step approach, feature extraction and recognition, we present an End-to-End (E2E) approach inside a deep neural network, and this has led to a significant improvement in audio-only, visual-only and audio-visual experiments. We further replace Bi-directional Gated Recurrent Unit (BGRU) with Temporal Convolutional Networks (TCN) to greatly simplify the training procedure. Secondly, we extend our AVSR model for continuous speech by presenting a hybrid Connectionist Temporal Classification (CTC)/Attention model, that can be trained in an end-to-end manner. We then propose the addition of prediction-based auxiliary tasks to a VSR model and highlight the importance of hyper-parameter optimisation and appropriate data augmentations. Next, we present a self-supervised framework, Learning visual speech Representations from Audio via self-supervision (LiRA). Specifically, we train a ResNet+Conformer model to predict acoustic features from unlabelled visual speech, and find that this pre-trained model can be leveraged towards word-level and sentence-level lip-reading. We also investigate the Lombard effect influence in an end-to-end AVSR system, which is the first work using end-to-end deep architectures and presents results on unseen speakers. We show that even if a relatively small amount of Lombard speech is added to the training set then the performance in a real scenario, where noisy Lombard speech is present, can be significantly improved. Lastly, we propose a detection method against adversarial examples in an AVSR system, where the strong correlation between audio and visual streams is leveraged. The synchronisation confidence score is leveraged as a proxy for audio-visual correlation and based on it, we can detect adversarial attacks. We apply recent adversarial attacks on two AVSR models and the experimental results demonstrate that the proposed approach is an effective way for detecting such attacks.Open Acces

Effective Low-Cost Time-Domain Audio Separation Using Globally Attentive Locally Recurrent Networks

Recent research on the time-domain audio separation networks (TasNets) has brought great success to speech separation. Nevertheless, conventional TasNets struggle to satisfy the memory and latency constraints in industrial applications. In this regard, we design a low-cost high-performance architecture, namely, globally attentive locally recurrent (GALR) network. Alike the dual-path RNN (DPRNN), we first split a feature sequence into 2D segments and then process the sequence along both the intra- and inter-segment dimensions. Our main innovation lies in that, on top of features recurrently processed along the inter-segment dimensions, GALR applies a self-attention mechanism to the sequence along the inter-segment dimension, which aggregates context-aware information and also enables parallelization. Our experiments suggest that GALR is a notably more effective network than the prior work. On one hand, with only 1.5M parameters, it has achieved comparable separation performance at a much lower cost with 36.1% less runtime memory and 49.4% fewer computational operations, relative to the DPRNN. On the other hand, in a comparable model size with DPRNN, GALR has consistently outperformed DPRNN in three datasets, in particular, with a substantial margin of 2.4dB absolute improvement of SI-SNRi in the benchmark WSJ0-2mix task

Robust Deep Learning Frameworks for Acoustic Scene and Respiratory Sound Classification

Although research on Acoustic Scene Classification (ASC) is very close to, or even overshadowed by different popular research areas known as Automatic Speech Recognition (ASR), Speaker Recognition (SR) or Image Processing (IP), this field potentially opens up several distinct and meaningful application areas based on environment context detection. The challenges of ASC mainly come from different noise resources, various sounds in real-world environments, occurring as single sounds, continuous sounds or overlapping sounds. In comparison to speech, sound scenes are more challenging mainly due to their being unstructured in form and closely similar to noise in certain contexts. Although a wide range of publications have focused on ASC recently, they show task-specific ways that either explore certain aspects of an ASC system or are evaluated on limited acoustic scene datasets. Therefore, the aim of this thesis is to contribute to the development of a robust framework to be applied for ASC, evaluated on various recently published datasets, and to achieve competitive performance compared to the state-of-the-art systems. To do this, a baseline model is firstly introduced. Next, extensive experiments on the baseline are conducted to identify key factors affecting final classification accuracy. From the comprehensive analysis, a robust deep learning framework, namely the Encoder-Decoder structure, is proposed to address three main factors that directly affect an ASC system. These factors comprise low-level input features, high-level feature extraction methodologies, and architectures for final classification. Within the proposed framework, three spectrogram transformations, namely Constant Q Transform (CQT), gammatone filter (Gamma), and log-mel, are used to convert recorded audio signals into spectrogram representations that resemble two-dimensional images. These three spectrograms used are referred to as low-level input features. To extract high-level features from spectrograms, a novel Encoder architecture, based on Convolutional Neural Networks, is proposed. In terms of the Decoder, also referred as to the final classifier, various models such as Random Forest Classifier, Deep Neural Network and Mixture of Experts, are evaluated and structured to obtain the best performance. To further improve an ASC system's performance, a scheme of two-level hierarchical classification, replacing the role of Decoder classification recently mentioned, is proposed. This scheme is useful to transform an ASC task over all categories into multiple ASC sub-tasks, each spanning fewer categories, in a divide-and- conquer strategy. At the highest level of the proposed scheme, meta-categories of acoustic scene sounds showing similar characteristics are classified. Next, categories within each meta-category are classified at the second level. Furthermore, an analysis of loss functions applied to different classifiers is conducted. This analysis indicates that a combination of entropy loss and triplet loss is useful to enhance performance, especially with tasks that comprise fewer categories. Further exploring ASC in terms of potential application to the health services, this thesis also explores the 2017 Internal Conference on Biomedical Health Informatics (ICBHI) benchmark dataset of lung sounds. A deep-learning frame- work, based on our novel ASC approaches, is proposed to classify anomaly cycles and predict respiratory diseases. The results obtained from these experiments show exceptional performance. This highlights the potential applications of using advanced ASC frameworks for early detection of auditory signals. In this case, signs of respiratory diseases, which could potentially be highly useful in future in directing treatment and preventing their spread