A Review of Deep Learning Techniques for Speech Processing

The field of speech processing has undergone a transformative shift with the advent of deep learning. The use of multiple processing layers has enabled the creation of models capable of extracting intricate features from speech data. This development has paved the way for unparalleled advancements in speech recognition, text-to-speech synthesis, automatic speech recognition, and emotion recognition, propelling the performance of these tasks to unprecedented heights. The power of deep learning techniques has opened up new avenues for research and innovation in the field of speech processing, with far-reaching implications for a range of industries and applications. This review paper provides a comprehensive overview of the key deep learning models and their applications in speech-processing tasks. We begin by tracing the evolution of speech processing research, from early approaches, such as MFCC and HMM, to more recent advances in deep learning architectures, such as CNNs, RNNs, transformers, conformers, and diffusion models. We categorize the approaches and compare their strengths and weaknesses for solving speech-processing tasks. Furthermore, we extensively cover various speech-processing tasks, datasets, and benchmarks used in the literature and describe how different deep-learning networks have been utilized to tackle these tasks. Additionally, we discuss the challenges and future directions of deep learning in speech processing, including the need for more parameter-efficient, interpretable models and the potential of deep learning for multimodal speech processing. By examining the field's evolution, comparing and contrasting different approaches, and highlighting future directions and challenges, we hope to inspire further research in this exciting and rapidly advancing field

Toward Multi-modal Multi-aspect Deep Alignment and Integration

Multi-modal/-aspect data contains complementary information about the same thing of interest that has the promising potential of leading to improved model robustness and thus gaining an increasing research focus. There are two typical categories of multi-modal/-aspect problems that require crossmodal/- aspect alignment and integration: 1) heterogeneous multi-modal problems that deal with data from multiple media forms, such as text, image etc., and 2) homogeneous multi-aspect problems that handle data with different aspects represented by the same media form, such as the syntactic and semantic aspects of a textual sentence etc. However, most of the existing approaches for multimodal/- aspect simply tackle the cross-modal/-aspect alignment and integration through various deep learning neural networks in an implicit manner and optimize based on the final task goals, leaving the potential strategies for improving the cross-modal/-aspect alignment and integration under-explored. This thesis aims to initiate an exploration of strategies and approaches towards multi-modal/-aspect deep alignment and integration. By looking into the limitations of existing approaches for both heterogeneous multi-modal problems and homogeneous multi-aspect problems, it proposes novel strategies and approaches for improving the cross-modal/-aspect alignment and integration and evaluates on the most essential representative tasks. For the heterogeneous setting, a cross-modal information captured graph-structured representation learning approach is proposed to enforce better cross-modal alignment and evaluated on the Language-to-Vision and Vision-and-Language scenarios. On the other hand, for the homogeneous setting, a bi-directional and deep crossintegration mechanism is explored to synthesise the multi-level semantics for comprehensive text understanding, which is validated in the joint multi-aspect natural language understanding context and its generalised text understanding setting

Towards Knowledge-Grounded Natural Language Understanding and Generation

This thesis investigates how natural language understanding and generation with transformer models can benefit from grounding the models with knowledge representations. Currently, the most prevailing paradigm for training language models is through pre-training on abundant raw text data and fine-tuning on downstream tasks. Although language models continue to advance, especially the recent trend of Large Language Models (LLMs) such as ChatGPT, there seem to be limits to what can be achieved with text data alone and it is desirable to study the impact of applying and integrating rich forms of knowledge representation to improve model performance. The most widely used form of knowledge for language modelling is structured knowledge in the form of triples consisting of entities and their relationships, often in English. This thesis explores beyond this conventional approach and aims to address several key questions: • Can knowledge of e ntities extend its benefits beyond entity-centric tasks such as entity linking? • How can we faithfully and effectively extract such structured knowledge from raw text, especially noisy web text? • How do other types of knowledge, beyond structured knowledge, contribute to improving NLP tasks? To this end, we study various tasks including multimodal and multilingual applications and consider a wide spectrum of knowledge, structured knowledge that is typically represented as triples with entities and their relations, and unstructured knowledge including parametric knowledge preserved in language models, knowledge distilled from Large Language Models, etc. Knowledge-grounding with structured knowledge. We begin by investigating the integration of structured knowledge into language models. Knowledge of entities has shown benefits for entity-centric tasks such as entity linking and relation extraction, however, most studies have been limited to monolingual settings. We expand knowledge-grounding with structured knowledge, specifically entities, in two directions of research. Firstly, we study whether knowledge of entities can benefit real-world fake news detection. We hypothesise that the world knowledge embedded in entities can contribute to assessing the truthfulness of news statements. Evaluation of various knowledge integration approaches on distinct datasets reveals that knowledge-enhanced language models improve fake news detection when incorporated with a relevant and up-to-date knowledge base. The second direction expands beyond English and focuses on multilingual entities. We introduce EntityCS, where we first construct a code-switched (CS) training corpus from Wikipedia, by switching entities in English to their counterparts in other languages. Then we intermediate-train a pretrained multilingual model on this corpus for joint masked language modelling and entity prediction. Subsequent fine-tuning of the model on entity-centric downstream tasks consistently improves zero-shot cross-lingual transferability, demonstrating the benefit of integrating knowledge of multilingual entities. Extracting structured knowledge from web text. We continue by studying effective, faithful, and robust extraction of structured knowledge from web text. Most existing information extraction (IE) datasets are constrained to Wikipedia articles, and models trained on such a rich factual text corpus show poor performance when applied to more noisy text from the web. To address these challenges, we introduce WebIE, a new dataset that takes raw sentences as input and structured triples as output. WebIE emphasises data quality by introducing negative examples and undergoing rigorous human annotation. We also propose faithful generative information extraction pipelines. Our experiments with entity planning training and prefix-trie decoding show improvement in accurately extracting knowledge on the web. Knowledge-grounding beyond structured knowledge. To address our last research question, we study the impact of a broader sense of knowledge, including parametric knowledge (knowledge stored in the latent parameters of the models) derived from a model’s self-explanations and knowledge distilled from LLMs via data augmentation. We expand the application to multimodal language models and study knowledge intensive visual question answering (VQA). We introduce a unified approach for fine-tuning multimodal models for jointly generating answers and explanations. Our experiments demonstrate enhancement in both answer accuracy and explanation quality. Lastly, as LLMs continue to advance in performance and size, we explore the utility of distilling common-sense knowledge from general-purpose LLMs to benefit smaller task-specific models. We prompt various LLMs to generate diverse examples on several challenging and scarce multilingual common sense datasets. This augmentation shows consistent enhancements on fine-tuned smaller models, shedding light on data augmentation strategies for scenarios with limited training data. In summary, this thesis explores the role of knowledge grounding in natural language understanding and generation across a broad spectrum of tasks. We found that incorporating relevant and up-to-date knowledge of entities benefits fake news detection, and entity-focused code-switching significantly enhances zero-shot cross lingual transfer on entity-centric tasks. In terms of effective and faithful approaches to extracting structured knowledge, our study found that integrating negative examples and training with entity planning significantly improves performance. Additionally, we established that other general forms of knowledge, such as parametric and distilled knowledge, enhance multimodal and multilingual knowledge intensive tasks. This research shows the tangible benefits of diverse knowledge integration and motivates further exploration in this direction

Dialogue systems based on pre-trained language models

Les modèles de langue pré-entraînés ont montré leur efficacité dans beaucoup de tâches de traitement de la langue naturelle. Ces modèles peuvent capter des régularités générales d'une langue à partir d'un grand ensemble de textes, qui sont utiles dans la plupart des applications en traitement de langue naturelle. Dans ce mémoire, nous étudions les problèmes de dialogue, i.e. générer une réponse à un énoncé de l'utilisateur. Nous exploitons les modèles de langue pré-entraînés pour traiter différents aspects des systèmes de dialogue. Premièrement, les modèles de langue pré-entraînés sont entraînés and utilisés dans les systèmes de dialogue de différentes façons. Il n'est pas clair quelle façon est la plus appropriée. Pour le dialogue orienté-tâche, l’approche de l'état de l'art pour le suivi de l'état de dialogue (Dialogue State Tracking) utilise BERT comme encodeur et empile un autre réseau de neurones récurrent (RNN) sur les sorties de BERT comme décodeur. Dans ce cas, seul l'encodeur peut bénéficier des modèles de langue pré-entraînés. Dans la première partie de ce mémoire, nous proposons une méthode qui utilise un seul modèle BERT pour l'encodeur et le décodeur, permettant ainsi un ajustement de paramètres plus efficace. Notre méthode atteint une performance qui dépasse l'état de l'art. Pour la tâche de génération de réponses dans un chatbot, nous comparons 4 approches communément utilisées. Elles sont basées sur des modèles pré-entraînés et utilisent des objectifs et des mécanismes d'attention différents. En nous appuyant sur des expérimentations, nous observons l'impact de deux types de disparité qui sont largement ignorées dans la littérature: disparité entre pré-entraînement et peaufinage, et disparité entre peaufinage et génération de réponse. Nous montrons que l'impact de ces disparités devient évident quand le volume de données d’entraînement est limité. Afin de remédier à ce problème, nous proposons deux méthodes qui réduisent les disparités, permettant d'améliorer la performance. Deuxièmement, même si les méthodes basées sur des modèles pré-entraînés ont connu de grands succès en dialogue général, nous devons de plus en plus traiter le problème de dialogue conditionné, c'est-à-dire dialogue en relation à une certaine condition (qui peut désigner un personnage, un sujet, etc.). Des chercheurs se sont aussi intéressés aux systèmes de chatbot avec des habiletés de conversation multiples, i.e. chatbot capable de confronter différentes situations de dialogues conditionnés. Ainsi, dans la seconde partie de ce mémoire, nous étudions le problème de génération de dialogue conditionné. D'abord, nous proposons une méthode générale qui exploite non seulement des données de dialogues conditionnées, mais aussi des données non-dialogues (textes) conditionnées. Ces dernières sont beaucoup plus faciles à acquérir en pratique. Ceci nous permet d'atténuer le problème de rareté de données. Ensuite, nous proposons des méthodes qui utilisent le concept d'adaptateur proposé récemment dans la littérature. Un adaptateur permet de renforcer un système de dialogue général en lui donnant une habileté spécifique. Nous montrons que les adaptateurs peuvent encoder des habiletés de dialogue conditionné de façon stricte ou flexible, tout en utilisant seulement 6% plus de paramètres. Ce mémoire contient 4 travaux sur deux grands problèmes de dialogue: l'architecture inhérente du modèle de dialogue basé sur des modèles de langue pré-entraînés, et l'enrichissement d'un système de dialogue général pour avoir des habiletés spécifiques. Ces travaux non seulement nous permettent d'obtenir des performances dépassant de l'état de l'art, mais aussi soulignent l'importance de concevoir l'architecture du modèle pour bien correspondre à la tâche, plutôt que simplement augmenter le volume de données d'entraînement et la puissance de calcul brute.Pre-trained language models (LMs) have shown to be effective in many NLP tasks. They can capture general language regularities from a large amount of texts, which are useful for most applications related to natural languages. In this thesis, we study the problems of dialogue, i.e. to generate a response to a user's utterance. We exploit pre-trained language models to deal with different aspects of dialogue systems. First, pre-trained language models have been trained and used in different ways in dialogue systems and it is unclear what is the best way to use pre-trained language models in dialogue. For task-oriented dialogue systems, the state-of-the-art framework for Dialogue State Tracking (DST) uses BERT as the encoder and stacks an RNN upon BERT outputs as the decoder. Pre-trained language models are only leveraged for the encoder. In the first part of the thesis, we investigate methods using a single BERT model for both the encoder and the decoder, allowing for more effective parameter updating. Our method achieves new state-of-the-art performance. For the task of response generation in generative chatbot systems, we further compare the 4 commonly used frameworks based on pre-trained LMs, which use different training objectives and attention mechanisms. Through extensive experiments, we observe the impact of two types of discrepancy: pretrain-finetune discrepancy and finetune-generation discrepancy (i.e. differences between pre-training and fine-tuning, and between fine-tuning and generation), which have not been paid attention to. We show that the impact of the discrepancies will surface when limited amount of training data is available. To alleviate the problem, we propose two methods to reduce discrepancies, yielding improved performance. Second, even though pre-training based methods have shown excellent performance in general dialogue generation, we are more and more faced with the problem of conditioned conversation, i.e. conversation in relation with some condition (persona, topic, etc.). Researchers are also interested in multi-skill chatbot systems, namely equipping a chatbot with abilities to confront different conditioned generation tasks. Therefore, in the second part of the thesis, we investigate the problem of conditioned dialogue generation. First, we propose a general method that leverages not only conditioned dialogue data, but also conditioned non-dialogue text data, which are much easier to collect, in order to alleviate the data scarcity issue of conditioned dialogue generation. Second, the concept of Adapter has been recently proposed, which adapts a general dialogue system to enhance some dialogue skill. We investigate the ways to learn a dialogue skill. We show that Adapter has enough capacity to model a dialogue skill for either loosely-conditioned or strictly-conditioned response generation, while using only 6% more parameters. This thesis contains 4 pieces of work relating to the two general problems in dialogue systems: the inherent architecture for dialogue systems based on pre-trained LMs, and enhancement of a general dialogue system for some specific skills. The studies not only propose new approaches that outperform the current state of the art, but also stress the importance of carefully designing the model architecture to fit the task, instead of simply increasing the amount of training data and the raw computation power

From Knowledge Augmentation to Multi-tasking: Towards Human-like Dialogue Systems

The goal of building dialogue agents that can converse with humans naturally has been a long-standing dream of researchers since the early days of artificial intelligence. The well-known Turing Test proposed to judge the ultimate validity of an artificial intelligence agent on the indistinguishability of its dialogues from humans'. It should come as no surprise that human-level dialogue systems are very challenging to build. But, while early effort on rule-based systems found limited success, the emergence of deep learning enabled great advance on this topic. In this thesis, we focus on methods that address the numerous issues that have been imposing the gap between artificial conversational agents and human-level interlocutors. These methods were proposed and experimented with in ways that were inspired by general state-of-the-art AI methodologies. But they also targeted the characteristics that dialogue systems possess.Comment: PhD thesi

Decouple knowledge from paramters for plug-and-play language modeling

Pre-trained language models(PLM) have made impressive results in various NLP tasks. It has been revealed that one of the key factors to their success is the parameters of these models implicitly learn all kinds of knowledge during pre-training. However, encoding knowledge implicitly in the model parameters has two fundamental drawbacks. First, the knowledge is neither editable nor scalable once the model is trained, which is especially problematic in that knowledge is consistently evolving. Second, it lacks interpretability and prevents humans from understanding which knowledge PLM requires for a certain problem. In this paper, we introduce PlugLM, a pre-training model with differentiable plug-in memory(DPM). The key intuition is to decouple the knowledge storage from model parameters with an editable and scalable key-value memory and leverage knowledge in an explainable manner by knowledge retrieval in the DPM. To justify this design choice, we conduct evaluations in three settings including: (1) domain adaptation. PlugLM obtains 3.95 F1 improvements across four domains on average without any in-domain pre-training. (2) knowledge update. PlugLM could absorb new knowledge in a training-free way after pre-training is done. (3) in-task knowledge learning. PlugLM could be further improved by incorporating training samples into DPM with knowledge prompting.Comment: ACL2023 Finding

Intelligent Techniques to Accelerate Everyday Text Communication

People with some form of speech- or motor-impairments usually use a high-tech augmentative and alternative communication (AAC) device to communicate with other people in writing or in face-to-face conversations. Their text entry rate on these devices is slow due to their motor abilities. Making good letter or word predictions can help accelerate the communication of such users. In this dissertation, we investigated several approaches to accelerate input for AAC users. First, considering that an AAC user is participating in a face-to-face conversation, we investigated whether performing speech recognition on the speaking-side can improve next word predictions. We compared the accuracy of three plausible microphone deployment options and the accuracy of two commercial speech recognition engines. We found that despite recognition word error rates of 7-16%, our ensemble of n-gram and recurrent neural network language models made predictions nearly as good as when they used the reference transcripts. In a user study with 160 participants, we also found that increasing number of prediction slots in a keyboard interface does not necessarily correlate to improved performance. Second, typing every character in a text message may require an AAC user more time or effort than strictly necessary. Skipping spaces or other characters may be able to speed input and reduce an AAC user\u27s physical input effort. We designed a recognizer optimized for expanding noisy abbreviated input where users often omitted spaces and mid-word vowels. We showed using neural language models for selecting conversational-style training text and for rescoring the recognizer\u27s n-best sentences improved accuracy. We found accurate abbreviated input was possible even if a third of characters was omitted. In a study where users had to dwell for a second on each key, we found sentence abbreviated input was competitive with a conventional keyboard with word predictions. Finally, AAC keyboards rely on language modeling to auto-correct noisy typing and to offer word predictions. While today language models can be trained on huge amounts of text, pre-trained models may fail to capture the unique writing style and vocabulary of individual users. We demonstrated improved performance compared to a unigram cache by adapting to a user\u27s text via language models based on prediction by partial match (PPM) and recurrent neural networks. Our best model ensemble increased keystroke savings by 9.6%

Contextual cues for deep learning models of code

Le code source offre un domaine d'application passionnant des méthodes d'apprentissage en profondeur, englobant des tâches telles que la synthèse, la réparation et l'analyse de programmes, ainsi que des tâches à l'intersection du code et du langage naturel. Bien que les modèles d’apprentissage profond pour le code, en particulier les grands modèles de langage, aient récemment connu un succès significatif, ils peuvent avoir du mal à se généraliser à du code invisible. Cela peut conduire à des inexactitudes, en particulier lorsque vous travaillez avec des référentiels contenant des logiciels propriétaires ou du code en cours de travail. L'objectif principal de cette thèse est d'exploiter efficacement les signaux utiles du contexte disponible afin d'améliorer les performances des modèles de code d'apprentissage profond pour une tâche donnée. En incorporant ces indices contextuels, les capacités de généralisation du modèle sont amplifiées, fournissant des informations supplémentaires non évidentes à partir de l'entrée d'origine et orientant son attention vers des détails essentiels. De plus, l'utilisation d'indices contextuels facilite l'adaptation aux nouvelles tâches et améliore les performances des tâches existantes en effectuant des prédictions plus contextuelles. Pour y parvenir, nous présentons un cadre général comprenant deux étapes : (a) l'amélioration du contexte, qui implique l'enrichissement de l'entrée avec un contexte de support obtenu grâce à l'identification et à la sélection d'indices contextuels pertinents, et (b) la prédiction à l'aide du contexte amélioré, où nous exploitez le contexte de support combiné aux entrées pour faire des prédictions précises. La thèse présente quatre articles qui proposent diverses approches pour ces étapes. Le premier article divise le problème standard de la programmation par exemples en deux étapes: (a) trouver des programmes qui satisfont des exemples individuels (solutions par exemple) et, (b) combiner ces solutions par exemple en tirant parti de leurs états d'exécution de programme pour trouver un programme qui satisfait tous les exemples donnés. Le deuxième article propose une approche pour sélectionner des informations ciblées à partir du fichier actuel et les utiliser pour adapter le modèle de complétion de code à un contexte local jamais vu précédemment. Le troisième article s'appuie sur le deuxième article en tirant parti des indices contextuels de l'ensemble du répertoire de code à l'aide d'un ensemble de requêtes ({\it prompts}) proposées suggérant l'emplacement et le contenu du contexte particulièrement utile à extraire du répertoire. Nous proposons un cadre pour sélectionner la requête la plus pertinente, qui est ensuite utilisée pour demander à un modèle de langage de code de générer des prédictions pour le reste de la ligne de code suivant un curseur positionné dans un fichier. Le quatrième article prolonge le troisième article en proposant un cadre qui apprend à combiner plusieurs contextes divers à partir du répertoire. Nous montrons que la formation de modèles de language de code plus petits de cette manière fonctionne mieux ou à égalité avec des modèles beaucoup plus grands qui n'utilisent pas le contexte du répertoire de code.Source code provides an exciting application area of deep learning methods, encompassing tasks like program synthesis, repair, and analysis, as well as tasks at the intersection of code and natural language. Although deep learning models for code, particularly large language models, have recently seen significant success, they can face challenges in generalizing to unseen code. This can lead to inaccuracies especially when working with repositories that contain proprietary software or work-in-progress code. The main focus of this thesis is to effectively harness useful signals from the available context such that it can improve the performance of the deep learning models of code at the given task. By incorporating these contextual cues, the model's generalization capabilities are amplified, providing additional insights not evident from the original input and directing its focus toward essential details. Furthermore, the use of contextual cues aids in adapting to new tasks and boosts performance on existing ones by making more context-aware predictions. To achieve this, we present a general framework comprising two stages: (a) Context Enhancement, which involves enriching the input with support context obtained through the identification and selection of relevant contextual cues, and (b) Prediction using the Enhanced Context, where we leverage the support context combined with the input to make accurate predictions. The thesis presents four articles that propose diverse approaches for these stages. The first article breaks the standard problem of programming by examples into two stages: (a) finding programs that satisfy individual examples (per-example solutions) and, (b) combining these per-example solutions by leveraging their program execution states to find a program that satisfies all given examples. The second article proposes an approach for selecting targeted information from the current file and using it to adapt the code completion model to an unseen, local context. The third article builds upon the second article by leveraging contextual cues from the entire code repository using a set of prompt proposals that govern the location and content of the context that should be taken from the repository. We propose a framework to select the most relevant prompt proposal context which is then used to prompt a large language model of code to generate predictions for the tokens in the rest of the line following the cursor in a file. The fourth article extends the third article by proposing a framework that learns to combine multiple diverse contexts from the repository. We show that training smaller models of code this way performs better or at par with significantly larger models that are not trained with repository context