Continuous Speech Recognition of Kazakh Language

This article describes the methods of creating a system of recognizing the continuous speech of Kazakh language. Studies on recognition of Kazakh speech in comparison with other languages began relatively recently, that is after obtaining independence of the country, and belongs to low resource languages. A large amount of data is required to create a reliable system and evaluate it accurately. A database has been created for the Kazakh language, consisting of a speech signal and corresponding transcriptions. The continuous speech has been composed of 200 speakers of different genders and ages, and the pronunciation vocabulary of the selected language. Traditional models and deep neural networks have been used to train the system. As a result, a word error rate (WER) of 30.01% has been obtained

Linear mappings: semantic transfer from transformer models for cognate detection and coreference resolution

Includes bibliographical references.2022 Fall.Embeddings or vector representations of language and their properties are useful for understanding how Natural Language Processing technology works. The usefulness of embeddings, however, depends on how contextualized or information-rich such embeddings are. In this work, I apply a novel affine (linear) mapping technique first established in the field of computer vision to embeddings generated from large Transformer-based language models. In particular, I study its use in two challenging linguistic tasks: cross-lingual cognate detection and cross-document coreference resolution. Cognate detection for two Low-Resource Languages (LRL), Assamese and Bengali, is framed as a binary classification problem using semantic (embedding-based), articulatory, and phonetic features. Linear maps for this task are extrinsically evaluated on the extent of transfer of semantic information between monolingual as well as multi-lingual models including those specialized for low-resourced Indian languages. For cross-document coreference resolution, whole-document contextual representations are generated for event and entity mentions from cross- document language models like CDLM and other BERT-variants and then linearly mapped to form coreferring clusters based on their cosine similarities. I evaluate my results on gold output based on established coreference metrics like BCUB and MUC. My findings reveal that linearly transforming vectors from one model's embedding space to another carries certain semantic information with high fidelity thereby revealing the existence of a canonical embedding space and its geometric properties for language models. Interestingly, even for a much more challenging task like coreference resolution, linear maps are able to transfer semantic information between "lighter" models or less contextual models and "larger" models with near-equivalent performance or even improved results in some cases

An exploration of the rhythm of Malay

In recent years there has been a surge of interest in speech rhythm. However we still lack a clear understanding of the nature of rhythm and rhythmic differences across languages. Various metrics have been proposed as means for measuring rhythm on the phonetic level and making typological comparisons between languages (Ramus et al, 1999; Grabe & Low, 2002; Dellwo, 2006) but the debate is ongoing on the extent to which these metrics capture the rhythmic basis of speech (Arvaniti, 2009; Fletcher, in press). Furthermore, cross linguistic studies of rhythm have covered a relatively small number of languages and research on previously unclassified languages is necessary to fully develop the typology of rhythm. This study examines the rhythmic features of Malay, for which, to date, relatively little work has been carried out on aspects rhythm and timing. The material for the analysis comprised 10 sentences produced by 20 speakers of standard Malay (10 males and 10 females). The recordings were first analysed using rhythm metrics proposed by Ramus et. al (1999) and Grabe & Low (2002). These metrics (∆C, %V, rPVI, nPVI) are based on durational measurements of vocalic and consonantal intervals. The results indicated that Malay clustered with other so-called syllable-timed languages like French and Spanish on the basis of all metrics. However, underlying the overall findings for these metrics there was a large degree of variability in values across speakers and sentences, with some speakers having values in the range typical of stressed-timed languages like English. Further analysis has been carried out in light of Fletcher’s (in press) argument that measurements based on duration do not wholly reflect speech rhythm as there are many other factors that can influence values of consonantal and vocalic intervals, and Arvaniti’s (2009) suggestion that other features of speech should also be considered in description of rhythm to discover what contributes to listeners’ perception of regularity. Spectrographic analysis of the Malay recordings brought to light two parameters that displayed consistency and regularity for all speakers and sentences: the duration of individual vowels and the duration of intervals between intensity minima. This poster presents the results of these investigations and points to connections between the features which seem to be consistently regulated in the timing of Malay connected speech and aspects of Malay phonology. The results are discussed in light of current debate on the descriptions of rhythm

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

Cross-view Embeddings for Information Retrieval

In this dissertation, we deal with the cross-view tasks related to information retrieval using embedding methods. We study existing methodologies and propose new methods to overcome their limitations. We formally introduce the concept of mixed-script IR, which deals with the challenges faced by an IR system when a language is written in different scripts because of various technological and sociological factors. Mixed-script terms are represented by a small and finite feature space comprised of character n-grams. We propose the cross-view autoencoder (CAE) to model such terms in an abstract space and CAE provides the state-of-the-art performance. We study a wide variety of models for cross-language information retrieval (CLIR) and propose a model based on compositional neural networks (XCNN) which overcomes the limitations of the existing methods and achieves the best results for many CLIR tasks such as ad-hoc retrieval, parallel sentence retrieval and cross-language plagiarism detection. We empirically test the proposed models for these tasks on publicly available datasets and present the results with analyses. In this dissertation, we also explore an effective method to incorporate contextual similarity for lexical selection in machine translation. Concretely, we investigate a feature based on context available in source sentence calculated using deep autoencoders. The proposed feature exhibits statistically significant improvements over the strong baselines for English-to-Spanish and English-to-Hindi translation tasks. Finally, we explore the the methods to evaluate the quality of autoencoder generated representations of text data and analyse its architectural properties. For this, we propose two metrics based on reconstruction capabilities of the autoencoders: structure preservation index (SPI) and similarity accumulation index (SAI). We also introduce a concept of critical bottleneck dimensionality (CBD) below which the structural information is lost and present analyses linking CBD and language perplexity.En esta disertación estudiamos problemas de vistas-múltiples relacionados con la recuperación de información utilizando técnicas de representación en espacios de baja dimensionalidad. Estudiamos las técnicas existentes y proponemos nuevas técnicas para solventar algunas de las limitaciones existentes. Presentamos formalmente el concepto de recuperación de información con escritura mixta, el cual trata las dificultades de los sistemas de recuperación de información cuando los textos contienen escrituras en distintos alfabetos debido a razones tecnológicas y socioculturales. Las palabras en escritura mixta son representadas en un espacio de características finito y reducido, compuesto por n-gramas de caracteres. Proponemos los auto-codificadores de vistas-múltiples (CAE, por sus siglas en inglés) para modelar dichas palabras en un espacio abstracto, y esta técnica produce resultados de vanguardia. En este sentido, estudiamos varios modelos para la recuperación de información entre lenguas diferentes (CLIR, por sus siglas en inglés) y proponemos un modelo basado en redes neuronales composicionales (XCNN, por sus siglas en inglés), el cual supera las limitaciones de los métodos existentes. El método de XCNN propuesto produce mejores resultados en diferentes tareas de CLIR tales como la recuperación de información ad-hoc, la identificación de oraciones equivalentes en lenguas distintas y la detección de plagio entre lenguas diferentes. Para tal efecto, realizamos pruebas experimentales para dichas tareas sobre conjuntos de datos disponibles públicamente, presentando los resultados y análisis correspondientes. En esta disertación, también exploramos un método eficiente para utilizar similitud semántica de contextos en el proceso de selección léxica en traducción automática. Específicamente, proponemos características extraídas de los contextos disponibles en las oraciones fuentes mediante el uso de auto-codificadores. El uso de las características propuestas demuestra mejoras estadísticamente significativas sobre sistemas de traducción robustos para las tareas de traducción entre inglés y español, e inglés e hindú. Finalmente, exploramos métodos para evaluar la calidad de las representaciones de datos de texto generadas por los auto-codificadores, a la vez que analizamos las propiedades de sus arquitecturas. Como resultado, proponemos dos nuevas métricas para cuantificar la calidad de las reconstrucciones generadas por los auto-codificadores: el índice de preservación de estructura (SPI, por sus siglas en inglés) y el índice de acumulación de similitud (SAI, por sus siglas en inglés). También presentamos el concepto de dimensión crítica de cuello de botella (CBD, por sus siglas en inglés), por debajo de la cual la información estructural se deteriora. Mostramos que, interesantemente, la CBD está relacionada con la perplejidad de la lengua.En aquesta dissertació estudiem els problemes de vistes-múltiples relacionats amb la recuperació d'informació utilitzant tècniques de representació en espais de baixa dimensionalitat. Estudiem les tècniques existents i en proposem unes de noves per solucionar algunes de les limitacions existents. Presentem formalment el concepte de recuperació d'informació amb escriptura mixta, el qual tracta les dificultats dels sistemes de recuperació d'informació quan els textos contenen escriptures en diferents alfabets per motius tecnològics i socioculturals. Les paraules en escriptura mixta són representades en un espai de característiques finit i reduït, composat per n-grames de caràcters. Proposem els auto-codificadors de vistes-múltiples (CAE, per les seves sigles en anglès) per modelar aquestes paraules en un espai abstracte, i aquesta tècnica produeix resultats d'avantguarda. En aquest sentit, estudiem diversos models per a la recuperació d'informació entre llengües diferents (CLIR , per les sevas sigles en anglès) i proposem un model basat en xarxes neuronals composicionals (XCNN, per les sevas sigles en anglès), el qual supera les limitacions dels mètodes existents. El mètode de XCNN proposat produeix millors resultats en diferents tasques de CLIR com ara la recuperació d'informació ad-hoc, la identificació d'oracions equivalents en llengües diferents, i la detecció de plagi entre llengües diferents. Per a tal efecte, realitzem proves experimentals per aquestes tasques sobre conjunts de dades disponibles públicament, presentant els resultats i anàlisis corresponents. En aquesta dissertació, també explorem un mètode eficient per utilitzar similitud semàntica de contextos en el procés de selecció lèxica en traducció automàtica. Específicament, proposem característiques extretes dels contextos disponibles a les oracions fonts mitjançant l'ús d'auto-codificadors. L'ús de les característiques proposades demostra millores estadísticament significatives sobre sistemes de traducció robustos per a les tasques de traducció entre anglès i espanyol, i anglès i hindú. Finalment, explorem mètodes per avaluar la qualitat de les representacions de dades de text generades pels auto-codificadors, alhora que analitzem les propietats de les seves arquitectures. Com a resultat, proposem dues noves mètriques per quantificar la qualitat de les reconstruccions generades pels auto-codificadors: l'índex de preservació d'estructura (SCI, per les seves sigles en anglès) i l'índex d'acumulació de similitud (SAI, per les seves sigles en anglès). També presentem el concepte de dimensió crítica de coll d'ampolla (CBD, per les seves sigles en anglès), per sota de la qual la informació estructural es deteriora. Mostrem que, de manera interessant, la CBD està relacionada amb la perplexitat de la llengua.Gupta, PA. (2017). Cross-view Embeddings for Information Retrieval [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/78457TESI

Evaluating Information Retrieval and Access Tasks

This open access book summarizes the first two decades of the NII Testbeds and Community for Information access Research (NTCIR). NTCIR is a series of evaluation forums run by a global team of researchers and hosted by the National Institute of Informatics (NII), Japan. The book is unique in that it discusses not just what was done at NTCIR, but also how it was done and the impact it has achieved. For example, in some chapters the reader sees the early seeds of what eventually grew to be the search engines that provide access to content on the World Wide Web, today’s smartphones that can tailor what they show to the needs of their owners, and the smart speakers that enrich our lives at home and on the move. We also get glimpses into how new search engines can be built for mathematical formulae, or for the digital record of a lived human life. Key to the success of the NTCIR endeavor was early recognition that information access research is an empirical discipline and that evaluation therefore lay at the core of the enterprise. Evaluation is thus at the heart of each chapter in this book. They show, for example, how the recognition that some documents are more important than others has shaped thinking about evaluation design. The thirty-three contributors to this volume speak for the many hundreds of researchers from dozens of countries around the world who together shaped NTCIR as organizers and participants. This book is suitable for researchers, practitioners, and students—anyone who wants to learn about past and present evaluation efforts in information retrieval, information access, and natural language processing, as well as those who want to participate in an evaluation task or even to design and organize one