Sparse and Low-rank Modeling for Automatic Speech Recognition

This thesis deals with exploiting the low-dimensional multi-subspace structure of speech towards the goal of improving acoustic modeling for automatic speech recognition (ASR). Leveraging the parsimonious hierarchical nature of speech, we hypothesize that whenever a speech signal is measured in a high-dimensional feature space, the true class information is embedded in low-dimensional subspaces whereas noise is scattered as random high-dimensional erroneous estimations in the features. In this context, the contribution of this thesis is twofold: (i) identify sparse and low-rank modeling approaches as excellent tools for extracting the class-specific low-dimensional subspaces in speech features, and (ii) employ these tools under novel ASR frameworks to enrich the acoustic information present in the speech features towards the goal of improving ASR. Techniques developed in this thesis focus on deep neural network (DNN) based posterior features which, under the sparse and low-rank modeling approaches, unveil the underlying class-specific low-dimensional subspaces very elegantly. In this thesis, we tackle ASR tasks of varying difficulty, ranging from isolated word recognition (IWR) and connected digit recognition (CDR) to large-vocabulary continuous speech recognition (LVCSR). For IWR and CDR, we propose a novel \textit{Compressive Sensing} (CS) perspective towards ASR. Here exemplar-based speech recognition is posed as a problem of recovering sparse high-dimensional word representations from compressed low-dimensional phonetic representations. In the context of LVCSR, this thesis argues that albeit their power in representation learning, DNN based acoustic models still have room for improvement in exploiting the \textit{union of low-dimensional subspaces} structure of speech data. Therefore, this thesis proposes to enhance DNN posteriors by projecting them onto the manifolds of the underlying classes using principal component analysis (PCA) or compressive sensing based dictionaries. Projected posteriors are shown to be more accurate training targets for learning better acoustic models, resulting in improved ASR performance. The proposed approach is evaluated on both close-talk and far-field conditions, confirming the importance of sparse and low-rank modeling of speech in building a robust ASR framework. Finally, the conclusions of this thesis are further consolidated by an information theoretic analysis approach which explicitly quantifies the contribution of proposed techniques in improving ASR

A Non-Coercing Account of Event Structure in Pular

In the light of recent theories of event structure and focus interpretation, this thesis proposes an analysis of the semantic and pragmatic values of the main perfective forms of Pular, a dialect of the Fulfulde language of the West Atlantic branch of the Niger-Congo language family. The perfective system of Pular shows considerable complexity, there being three basic forms and several other forms with additional morphological marking. The semantic and pragmatic distinction between these forms has up till now resisted satisfactory analysis. Three basic forms and an additional statively-marked form are considered to constitute the four main forms in the Pular perfective system. An analysis of these forms is proposed in which it is argued that the notion of coercion, found in other proposals on event structure, is inappropriate and its function can be replaced by the use of a richer event structure of events and sub-events. To capture the appropriate interpretation of the perfective forms, a Neo- Davidsonian logical form is used, building on and elaborating ideas of Higginbotham, in which both event structure and verb focus can be portrayed. This type of logical form is justified in terms of the Carlsonian Kind/Object/Stage distinction, and is considered to portray the mental representation of an utterance in conceptual structure, in the sense of Jackendoff’s proposals on the architecture of the language faculty. Further, in conjunction with relevance-theoretic principles of utterance interpretation, Rooth's alternative semantics theory of focus is deployed to show how these logical forms are interpreted and considered felicitous or infelicitous in different contexts. This analysis of the Pular perfective verb system shows how a layered ontology of events and sub-events can be used to analyse a complex perfective system and constitutes an advance on previous studies of aspectual phenomena in Fulfulde

Speech analysis using very low-dimensional bottleneck features and phone-class dependent neural networks

The first part of this thesis focuses on very low-dimensional bottleneck features (BNFs), extracted from deep neural networks (DNNs) for speech analysis and recognition. Very low-dimensional BNFs are analysed in terms of their capability of representing speech and their suitability for modelling speech dynamics. Nine-dimensional BNFs obtained from a phone discrimination DNN are shown to give comparable phone recognition accuracy to 39-dimensional MFCCs, and an average of 34% higher phone recognition accuracy than formant-based features of the same dimensions. They also preserve the trajectory continuity well and thus hold promise for modelling speech dynamics. Visualisations and interpretations of the BNFs are presented, with phonetically motivated studies of the strategies that DNNs employ to create these features. The relationships between BNF representations resulting from different initialisations of DNNs are explored. The second part of this thesis considers BNFs from the perspective of feature extraction. It is motivated by the observation that different types of speech sounds lend themselves to different acoustic analysis, and that the mapping from spectra-in-context to phone posterior probabilities implemented by the DNN is a continuous approximation to a discontinuous function. This suggests that it may be advantageous to replace the single DNN with a set of phone class dependent DNNs. In this case, the appropriate mathematical structure is a manifold. It is shown that this approach leads to significant improvements in frame level phone classification accuracy

Machine Learning for Information Retrieval

In this thesis, we explore the use of machine learning techniques for information retrieval. More specifically, we focus on ad-hoc retrieval, which is concerned with searching large corpora to identify the documents relevant to user queries. Thisidentification is performed through a ranking task. Given a user query, an ad-hoc retrieval system ranks the corpus documents, so that the documents relevant to the query ideally appear above the others. In a machine learning framework, we are interested in proposing learning algorithms that can benefit from limited training data in order to identify a ranker likely to achieve high retrieval performance over unseen documents and queries. This problem presents novel challenges compared to traditional learning tasks, such as regression or classification. First, our task is a ranking problem, which means that the loss for a given query cannot be measured as a sum of an individual loss suffered for each corpus document. Second, most retrieval queries present a highly unbalanced setup, with a set of relevant documents accounting only for a very small fraction of the corpus. Third, ad-hoc retrieval corresponds to a kind of ``double'' generalization problem, since the learned model should not only generalize to new documents but also to new queries. Finally, our task also presents challenging efficiency constraints, since ad-hoc retrieval is typically applied to large corpora. % The main objective of this thesis is to investigate the discriminative learning of ad-hoc retrieval models. For that purpose, we propose different models based on kernel machines or neural networks adapted to different retrieval contexts. The proposed approaches rely on different online learning algorithms that allow efficient learning over large corpora. The first part of the thesis focus on text retrieval. In this case, we adopt a classical approach to the retrieval ranking problem, and order the text documents according to their estimated similarity to the text query. The assessment of semantic similarity between text items plays a key role in that setup and we propose a learning approach to identify an effective measure of text similarity. This identification is not performed relying on a set of queries with their corresponding relevant document sets, since such data are especially expensive to label and hence rare. Instead, we propose to rely on hyperlink data, since hyperlinks convey semantic proximity information that is relevant to similarity learning. This setup is hence a transfer learning setup, where we benefit from the proximity information encoded by hyperlinks to improve the performance over the ad-hoc retrieval task. We then investigate another retrieval problem, i.e. the retrieval of images from text queries. Our approach introduces a learning procedure optimizing a criterion related to the ranking performance. This criterion adapts our previous learning objective for learning textual similarity to the image retrieval problem. This yields an image ranking model that addresses the retrieval problem directly. This approach contrasts with previous research that rely on an intermediate image annotation task. Moreover, our learning procedure builds upon recent work on the online learning of kernel-based classifiers. This yields an efficient, scalable algorithm, which can benefit from recent kernels developed for image comparison. In the last part of the thesis, we show that the objective function used in the previous retrieval problems can be applied to the task of keyword spotting, i.e. the detection of given keywords in speech utterances. For that purpose, we formalize this problem as a ranking task: given a keyword, the keyword spotter should order the utterances so that the utterances containing the keyword appear above the others. Interestingly, this formulation yields an objective directly maximizing the area under the receiver operating curve, the most common keyword spotter evaluation measure. This objective is then used to train a model adapted to this intrinsically sequential problem. This model is then learned with a procedure derived from the algorithm previously introduced for the image retrieval task. To conclude, this thesis introduces machine learning approaches for ad-hoc retrieval. We propose learning models for various multi-modal retrieval setups, i.e. the retrieval of text documents from text queries, the retrieval of images from text queries and the retrieval of speech recordings from written keywords. Our approaches rely on discriminative learning and enjoy efficient training procedures, which yields effective and scalable models. In all cases, links with prior approaches were investigated and experimental comparisons were conducted

Essential Speech and Language Technology for Dutch: Results by the STEVIN-programme

Computational Linguistics; Germanic Languages; Artificial Intelligence (incl. Robotics); Computing Methodologie

Proceedings of the Eighth Italian Conference on Computational Linguistics CliC-it 2021

The eighth edition of the Italian Conference on Computational Linguistics (CLiC-it 2021) was held at Università degli Studi di Milano-Bicocca from 26th to 28th January 2022. After the edition of 2020, which was held in fully virtual mode due to the health emergency related to Covid-19, CLiC-it 2021 represented the first moment for the Italian research community of Computational Linguistics to meet in person after more than one year of full/partial lockdown

Grapheme-based Automatic Speech Recognition using Probabilistic Lexical Modeling

Automatic speech recognition (ASR) systems incorporate expert knowledge of language or the linguistic expertise through the use of phone pronunciation lexicon (or dictionary) where each word is associated with a sequence of phones. The creation of phone pronunciation lexicon for a new language or domain is costly as it requires linguistic expertise, and includes time and money. In this thesis, we focus on effective building of ASR systems in the absence of linguistic expertise for a new domain or language. Particularly, we consider graphemes as alternate subword units for speech recognition. In a grapheme lexicon, pronunciation of a word is derived from its orthography. However, modeling graphemes for speech recognition is a challenging task for two reasons. Firstly, grapheme-to-phoneme (G2P) relationship can be ambiguous as languages continue to evolve after their spelling has been standardized. Secondly, as elucidated in this thesis, typically ASR systems directly model the relationship between graphemes and acoustic features; and the acoustic features depict the envelope of speech, which is related to phones. In this thesis, a grapheme-based ASR approach is proposed where the modeling of the relationship between graphemes and acoustic features is factored through a latent variable into two models, namely, acoustic model and lexical model. In the acoustic model the relationship between latent variables and acoustic features is modeled, while in the lexical model a probabilistic relationship between latent variables and graphemes is modeled. We refer to the proposed approach as probabilistic lexical modeling based ASR. In the thesis we show that the latent variables can be phones or multilingual phones or clustered context-dependent subword units; and an acoustic model can be trained on domain-independent or language-independent resources. The lexical model is trained on transcribed speech data from the target domain or language. In doing so, the parameters of the lexical model capture a probabilistic relationship between graphemes and phones. In the proposed grapheme-based ASR approach, lexicon learning is implicitly integrated as a phase in ASR system training as opposed to the conventional approach where first phone pronunciation lexicon is developed and then a phone-based ASR system is trained. The potential and the efficacy of the proposed approach is demonstrated through experiments and comparisons with other standard approaches on ASR for resource rich languages, nonnative and accented speech, under-resourced languages, and minority languages. The studies revealed that the proposed framework is particularly suitable when the task is challenged by the lack of both linguistic expertise and transcribed data. Furthermore, our investigations also showed that standard ASR approaches in which the lexical model is deterministic are more suitable for phones than graphemes, while probabilistic lexical model based ASR approach is suitable for both. Finally, we show that the captured grapheme-to-phoneme relationship can be exploited to perform acoustic data-driven G2P conversion

Discriminative preprocessing of speech : towards improving biometric authentication

Im Rahmen des "SecurePhone-Projektes" wurde ein multimodales System zur Benutzerauthentifizierung entwickelt, das auf ein PDA implementiert wurde. Bei der vollzogenen Erweiterung dieses Systems wurde der Möglichkeit nachgegangen, die Benutzerauthentifizierung durch eine auf biometrischen Parametern (E.: "feature enhancement") basierende Unterscheidung zwischen Sprechern sowie durch eine Kombination mehrerer Parameter zu verbessern. In der vorliegenden Dissertation wird ein allgemeines Bezugssystem zur Verbesserung der Parameter präsentiert, das ein mehrschichtiges neuronales Netz (E.: "MLP: multilayer perceptron") benutzt, um zu einer optimalen Sprecherdiskrimination zu gelangen. In einem ersten Schritt wird beim Trainieren des MLPs eine Teilmenge der Sprecher (Sprecherbasis) berücksichtigt, um die zugrundeliegenden Charakteristika des vorhandenen akustischen Parameterraums darzustellen. Am Ende eines zweiten Schrittes steht die Erkenntnis, dass die Größe der verwendeten Sprecherbasis die Leistungsfähigkeit eines Sprechererkennungssystems entscheidend beeinflussen kann. Ein dritter Schritt führt zur Feststellung, dass sich die Selektion der Sprecherbasis ebenfalls auf die Leistungsfähigkeit des Systems auswirken kann. Aufgrund dieser Beobachtung wird eine automatische Selektionsmethode für die Sprecher auf der Basis des maximalen Durchschnittswertes der Zwischenklassenvariation (between-class variance) vorgeschlagen. Unter Rückgriff auf verschiedene sprachliche Produktionssituationen (Sprachproduktion mit und ohne Hintergrundgeräusche; Sprachproduktion beim Telefonieren) wird gezeigt, dass diese Methode die Leistungsfähigkeit des Erkennungssystems verbessern kann. Auf der Grundlage dieser Ergebnisse wird erwartet, dass sich die hier für die Sprechererkennung verwendete Methode auch für andere biometrische Modalitäten als sinnvoll erweist. Zusätzlich wird in der vorliegenden Dissertation eine alternative Parameterrepräsentation vorgeschlagen, die aus der sog. "Sprecher-Stimme-Signatur" (E.: "SVS: speaker voice signature") abgeleitet wird. Die SVS besteht aus Trajektorien in einem Kohonennetz (E.: "SOM: self-organising map"), das den akustischen Raum repräsentiert. Als weiteres Ergebnis der Arbeit erweist sich diese Parameterrepräsentation als Ergänzung zu dem zugrundeliegenden Parameterset. Deshalb liegt eine Kombination beider Parametersets im Sinne einer Verbesserung der Leistungsfähigkeit des Erkennungssystems nahe. Am Ende der Arbeit sind schließlich einige potentielle Erweiterungsmöglichkeiten zu den vorgestellten Methoden zu finden. Schlüsselwörter: Feature Enhancement, MLP, SOM, Sprecher-Basis-Selektion, SprechererkennungIn the context of the SecurePhone project, a multimodal user authentication system was developed for implementation on a PDA. Extending this system, we investigate biometric feature enhancement and multi-feature fusion with the aim of improving user authentication accuracy. In this dissertation, a general framework for feature enhancement is proposed which uses a multilayer perceptron (MLP) to achieve optimal speaker discrimination. First, to train this MLP a subset of speakers (speaker basis) is used to represent the underlying characteristics of the given acoustic feature space. Second, the size of the speaker basis is found to be among the crucial factors affecting the performance of a speaker recognition system. Third, it is found that the selection of the speaker basis can also influence system performance. Based on this observation, an automatic speaker selection approach is proposed on the basis of the maximal average between-class variance. Tests in a variety of conditions, including clean and noisy as well as telephone speech, show that this approach can improve the performance of speaker recognition systems. This approach, which is applied here to feature enhancement for speaker recognition, can be expected to also be effective with other biometric modalities besides speech. Further, an alternative feature representation is proposed in this dissertation, which is derived from what we call speaker voice signatures (SVS). These are trajectories in a Kohonen self organising map (SOM) which has been trained to represent the acoustic space. This feature representation is found to be somewhat complementary to the baseline feature set, suggesting that they can be fused to achieve improved performance in speaker recognition. Finally, this dissertation finishes with a number of potential extensions of the proposed approaches. Keywords: feature enhancement, MLP, SOM, speaker basis selection, speaker recognition, biometric, authentication, verificatio

Learning cognitive maps: Finding useful structure in an uncertain world

In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg

Task-based parser output combination : workflow and infrastructure

This dissertation introduces the method of task-based parser output combination as a device to enhance the reliability of automatically generated syntactic information for further processing tasks. Parsers, i.e. tools generating syntactic analyses, are usually based on reference data. Typically these are modern news texts. However, the data relevant for applications or tasks beyond parsing often differs from this standard domain, or only specific phenomena from the syntactic analysis are actually relevant for further processing. In these cases, the reliability of the parsing output might deviate essentially from the expected outcome on standard news text. Studies for several levels of analysis in natural language processing have shown that combining systems from the same analysis level outperforms the best involved single system. This is due to different error distributions of the involved systems which can be exploited, e.g. in a majority voting approach. In other words: for an effective combination, the involved systems have to be sufficiently different. In these combination studies, usually the complete analyses are combined and evaluated. However, to be able to combine the analyses completely, a full mapping of their structures and tagsets has to be found. The need for a full mapping either restricts the degree to which the participating systems are allowed to differ or it results in information loss. Moreover, the evaluation of the combined complete analyses does not reflect the reliability achieved in the analysis of the specific aspects needed to resolve a given task. This work presents an abstract workflow which can be instantiated based on the respective task and the available parsers. The approach focusses on the task-relevant aspects and aims at increasing the reliability of their analysis. Moreover, this focus allows a combination of more diverging systems, since no full mapping of the structures and tagsets from the single systems is needed. The usability of this method is also increased by focussing on the output of the parsers: It is not necessary for the users to reengineer the tools. Instead, off-the-shelf parsers and parsers for which no configuration options or sources are available to the users can be included. Based on this, the method is applicable to a broad range of applications. For instance, it can be applied to tasks from the growing field of Digital Humanities, where the focus is often on tasks different from syntactic analysis