Holistic Vocabulary Independent Spoken Term Detection

Within this thesis, we aim at designing a loosely coupled holistic system for Spoken Term Detection (STD) on heterogeneous German broadcast data in selected application scenarios. Starting from STD on the 1-best output of a word-based speech recognizer, we study the performance of several subword units for vocabulary independent STD on a linguistically and acoustically challenging German corpus. We explore the typical error sources in subword STD, and find that they differ from the error sources in word-based speech search. We select, extend and combine a set of state-of-the-art methods for error compensation in STD in order to explicitly merge the corresponding STD error spaces through anchor-based approximate lattice retrieval. Novel methods for STD result verification are proposed in order to increase retrieval precision by exploiting external knowledge at search time. Error-compensating methods for STD typically suffer from high response times on large scale databases, and we propose scalable approaches suitable for large corpora. Highest STD accuracy is obtained by combining anchor-based approximate retrieval from both syllable lattice ASR and syllabified word ASR into a hybrid STD system, and pruning the result list using external knowledge with hybrid contextual and anti-query verification.Die vorliegende Arbeit beschreibt ein lose gekoppeltes, ganzheitliches System zur Sprachsuche auf heterogenenen deutschen Sprachdaten in unterschiedlichen Anwendungsszenarien. Ausgehend von einer wortbasierten Sprachsuche auf dem Transkript eines aktuellen Wort-Erkenners werden zunächst unterschiedliche Subwort-Einheiten für die vokabularunabhängige Sprachsuche auf deutschen Daten untersucht. Auf dieser Basis werden die typischen Fehlerquellen in der Subwort-basierten Sprachsuche analysiert. Diese Fehlerquellen unterscheiden sich vom Fall der klassichen Suche im Worttranskript und müssen explizit adressiert werden. Die explizite Kompensation der unterschiedlichen Fehlerquellen erfolgt durch einen neuartigen hybriden Ansatz zur effizienten Ankerbasierten unscharfen Wortgraph-Suche. Darüber hinaus werden neuartige Methoden zur Verifikation von Suchergebnissen vorgestellt, die zur Suchzeit verfügbares externes Wissen einbeziehen. Alle vorgestellten Verfahren werden auf einem umfangreichen Satz von deutschen Fernsehdaten mit Fokus auf ausgewählte, repräsentative Einsatzszenarien evaluiert. Da Methoden zur Fehlerkompensation in der Sprachsuchforschung typischerweise zu hohen Laufzeiten bei der Suche in großen Archiven führen, werden insbesondere auch Szenarien mit sehr großen Datenmengen betrachtet. Die höchste Suchleistung für Archive mittlerer Größe wird durch eine unscharfe und Anker-basierte Suche auf einem hybriden Index aus Silben-Wortgraphen und silbifizierter Wort-Erkennung erreicht, bei der die Suchergebnisse mit hybrider Verifikation bereinigt werden

Women in Artificial intelligence (AI)

This Special Issue, entitled "Women in Artificial Intelligence" includes 17 papers from leading women scientists. The papers cover a broad scope of research areas within Artificial Intelligence, including machine learning, perception, reasoning or planning, among others. The papers have applications to relevant fields, such as human health, finance, or education. It is worth noting that the Issue includes three papers that deal with different aspects of gender bias in Artificial Intelligence. All the papers have a woman as the first author. We can proudly say that these women are from countries worldwide, such as France, Czech Republic, United Kingdom, Australia, Bangladesh, Yemen, Romania, India, Cuba, Bangladesh and Spain. In conclusion, apart from its intrinsic scientific value as a Special Issue, combining interesting research works, this Special Issue intends to increase the invisibility of women in AI, showing where they are, what they do, and how they contribute to developments in Artificial Intelligence from their different places, positions, research branches and application fields. We planned to issue this book on the on Ada Lovelace Day (11/10/2022), a date internationally dedicated to the first computer programmer, a woman who had to fight the gender difficulties of her times, in the XIX century. We also thank the publisher for making this possible, thus allowing for this book to become a part of the international activities dedicated to celebrating the value of women in ICT all over the world. With this book, we want to pay homage to all the women that contributed over the years to the field of AI

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

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

The Lexicon Graph Model : a generic model for multimodal lexicon development

Trippel T. The Lexicon Graph Model : a generic model for multimodal lexicon development. Bielefeld (Germany): Bielefeld University; 2006.Das Lexicon Graph Model stellt ein Modell für Lexika dar, die korpusbasiert sein können und multimodale Informationen enthalten. Hierbei wird die Perspektive der Lexikontheorie eingenommen, wobei die zugrundeliegenden Datenstrukturen sowohl vom Lexikon als auch von Annotationen betrachtet werden. Letztere fallen dadurch in das Blickfeld, weil sie als Grundlage für die Erstellung von Lexika gesehen werden. Der Begriff des Lexikons bezieht sich hier sowohl auf den Bereich des Wörterbuchs als auch der in elektronischen Applikationen integrierten Lexikondatenbanken. Die existierenden Formalismen und Ansätze der Lexikonentwicklung zeigen verschiedene Probleme im Zusammenhang mit Lexika auf, etwa die Zusammenfassung von existierenden Lexika zu einem, die Disambiguierung von Mehrdeutigkeiten im Lexikon auf verschiedenen lexikalischen Ebenen, die Repräsentation von anderen Modalitäten im Lexikon, die Selektion des lexikalischen Schlüsselbegriffs für Lexikonartikel, etc. Der vorliegende Ansatz geht davon aus, dass sich Lexika zwar in ihrem Inhalt, nicht aber in einer grundlegenden Struktur unterscheiden, so dass verschiedenartige Lexika im Rahmen eines Unifikationsprozesses dublettenfrei miteinander verbunden werden können. Hieraus resultieren deklarative Lexika. Für Lexika können diese Graphen mit dem Lexikongraph-Modell wie hier dargestellt modelliert werden. Dabei sind Lexikongraphen analog den von Bird und Libermann beschriebenen Annotationsgraphen gesehen und können daher auch ähnlich verarbeitet werden. Die Untersuchung des Lexikonformalismus beruht auf vier Schritten. Zunächst werden existierende Lexika analysiert und beschrieben. Danach wird mit dem Lexikongraph-Modell eine generische Darstellung von Lexika vorgestellt, die auch implementiert und getestet wird. Basierend auf diesem Formalismus wird die Beziehung zu Annotationsgraphen hergestellt, wobei auch beschrieben wird, welche Maßstäbe an angemessene Annotationen für die Verwendung zur Lexikonentwicklung angelegt werden müssen.The Lexicon Graph Model provides a model and framework for lexicons that can be corpus based and contain multimodal information. The focus is more from the lexicon theory perspective, looking at the underlying data structures that are part of existing lexicons and corpora. The term lexicon in linguistics and artificial intelligence is used in different ways, including traditional print dictionaries in book form, CD-ROM editions, Web based versions of the same, but also computerized resources of similar structures to be used by applications. These applications cover systems for human-machine communication as well as spell checkers. The term lexicon in this work is used as the most generic term covering all lexical applications. Existing formalisms in lexicon development show different problems with lexicons, for example combining different kinds of lexical resources, disambiguation on different lexical levels, the representation of different modalities in a lexicon. The Lexicon Graph Model presupposes that lexicons can have different structures but have fundamentally a similar structure, making it possible to combine lexicons in a unification process, resulting in a declarative lexicon. The underlying model is a graph, the Lexicon Graph, which is modeled similar to Annotation Graphs as described by Bird and Libermann. The investigation of the lexicon formalism contains four steps, that is the analysis of existing lexicons, the introduction of the Lexicon Graph Model as a generic representation for lexicons, the implementation of the formalism in different contexts and an evaluation of the formalism. It is shown that Annotation Graphs and Lexicon Graphs are indeed related not only in their formalism and it is shown, what standards have to be applied to annotations to be usable for lexicon development