9,187 research outputs found

    Automatic Speech Recognition for Low-resource Languages and Accents Using Multilingual and Crosslingual Information

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    This thesis explores methods to rapidly bootstrap automatic speech recognition systems for languages, which lack resources for speech and language processing. We focus on finding approaches which allow using data from multiple languages to improve the performance for those languages on different levels, such as feature extraction, acoustic modeling and language modeling. Under application aspects, this thesis also includes research work on non-native and Code-Switching speech

    On adaptive decision rules and decision parameter adaptation for automatic speech recognition

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    Recent advances in automatic speech recognition are accomplished by designing a plug-in maximum a posteriori decision rule such that the forms of the acoustic and language model distributions are specified and the parameters of the assumed distributions are estimated from a collection of speech and language training corpora. Maximum-likelihood point estimation is by far the most prevailing training method. However, due to the problems of unknown speech distributions, sparse training data, high spectral and temporal variabilities in speech, and possible mismatch between training and testing conditions, a dynamic training strategy is needed. To cope with the changing speakers and speaking conditions in real operational conditions for high-performance speech recognition, such paradigms incorporate a small amount of speaker and environment specific adaptation data into the training process. Bayesian adaptive learning is an optimal way to combine prior knowledge in an existing collection of general models with a new set of condition-specific adaptation data. In this paper, the mathematical framework for Bayesian adaptation of acoustic and language model parameters is first described. Maximum a posteriori point estimation is then developed for hidden Markov models and a number of useful parameters densities commonly used in automatic speech recognition and natural language processing.published_or_final_versio

    Language Model Bootstrapping Using Neural Machine Translation For Conversational Speech Recognition

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    Building conversational speech recognition systems for new languages is constrained by the availability of utterances that capture user-device interactions. Data collection is both expensive and limited by the speed of manual transcription. In order to address this, we advocate the use of neural machine translation as a data augmentation technique for bootstrapping language models. Machine translation (MT) offers a systematic way of incorporating collections from mature, resource-rich conversational systems that may be available for a different language. However, ingesting raw translations from a general purpose MT system may not be effective owing to the presence of named entities, intra sentential code-switching and the domain mismatch between the conversational data being translated and the parallel text used for MT training. To circumvent this, we explore the following domain adaptation techniques: (a) sentence embedding based data selection for MT training, (b) model finetuning, and (c) rescoring and filtering translated hypotheses. Using Hindi as the experimental testbed, we translate US English utterances to supplement the transcribed collections. We observe a relative word error rate reduction of 7.8-15.6%, depending on the bootstrapping phase. Fine grained analysis reveals that translation particularly aids the interaction scenarios which are underrepresented in the transcribed data.Comment: Accepted by IEEE ASRU workshop, 201

    Improving Searchability of Automatically Transcribed Lectures Through Dynamic Language Modelling

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    Recording university lectures through lecture capture systems is increasingly common. However, a single continuous audio recording is often unhelpful for users, who may wish to navigate quickly to a particular part of a lecture, or locate a specific lecture within a set of recordings. A transcript of the recording can enable faster navigation and searching. Automatic speech recognition (ASR) technologies may be used to create automated transcripts, to avoid the significant time and cost involved in manual transcription. Low accuracy of ASR-generated transcripts may however limit their usefulness. In particular, ASR systems optimized for general speech recognition may not recognize the many technical or discipline-specific words occurring in university lectures. To improve the usefulness of ASR transcripts for the purposes of information retrieval (search) and navigating within recordings, the lexicon and language model used by the ASR engine may be dynamically adapted for the topic of each lecture. A prototype is presented which uses the English Wikipedia as a semantically dense, large language corpus to generate a custom lexicon and language model for each lecture from a small set of keywords. Two strategies for extracting a topic-specific subset of Wikipedia articles are investigated: a naïve crawler which follows all article links from a set of seed articles produced by a Wikipedia search from the initial keywords, and a refinement which follows only links to articles sufficiently similar to the parent article. Pair-wise article similarity is computed from a pre-computed vector space model of Wikipedia article term scores generated using latent semantic indexing. The CMU Sphinx4 ASR engine is used to generate transcripts from thirteen recorded lectures from Open Yale Courses, using the English HUB4 language model as a reference and the two topic-specific language models generated for each lecture from Wikipedia

    Contributions to Pen & Touch Human-Computer Interaction

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    [EN] Computers are now present everywhere, but their potential is not fully exploited due to some lack of acceptance. In this thesis, the pen computer paradigm is adopted, whose main idea is to replace all input devices by a pen and/or the fingers, given that the origin of the rejection comes from using unfriendly interaction devices that must be replaced by something easier for the user. This paradigm, that was was proposed several years ago, has been only recently fully implemented in products, such as the smartphones. But computers are actual illiterates that do not understand gestures or handwriting, thus a recognition step is required to "translate" the meaning of these interactions to computer-understandable language. And for this input modality to be actually usable, its recognition accuracy must be high enough. In order to realistically think about the broader deployment of pen computing, it is necessary to improve the accuracy of handwriting and gesture recognizers. This thesis is devoted to study different approaches to improve the recognition accuracy of those systems. First, we will investigate how to take advantage of interaction-derived information to improve the accuracy of the recognizer. In particular, we will focus on interactive transcription of text images. Here the system initially proposes an automatic transcript. If necessary, the user can make some corrections, implicitly validating a correct part of the transcript. Then the system must take into account this validated prefix to suggest a suitable new hypothesis. Given that in such application the user is constantly interacting with the system, it makes sense to adapt this interactive application to be used on a pen computer. User corrections will be provided by means of pen-strokes and therefore it is necessary to introduce a recognizer in charge of decoding this king of nondeterministic user feedback. However, this recognizer performance can be boosted by taking advantage of interaction-derived information, such as the user-validated prefix. Then, this thesis focuses on the study of human movements, in particular, hand movements, from a generation point of view by tapping into the kinematic theory of rapid human movements and the Sigma-Lognormal model. Understanding how the human body generates movements and, particularly understand the origin of the human movement variability, is important in the development of a recognition system. The contribution of this thesis to this topic is important, since a new technique (which improves the previous results) to extract the Sigma-lognormal model parameters is presented. Closely related to the previous work, this thesis study the benefits of using synthetic data as training. The easiest way to train a recognizer is to provide "infinite" data, representing all possible variations. In general, the more the training data, the smaller the error. But usually it is not possible to infinitely increase the size of a training set. Recruiting participants, data collection, labeling, etc., necessary for achieving this goal can be time-consuming and expensive. One way to overcome this problem is to create and use synthetically generated data that looks like the human. We study how to create these synthetic data and explore different approaches on how to use them, both for handwriting and gesture recognition. The different contributions of this thesis have obtained good results, producing several publications in international conferences and journals. Finally, three applications related to the work of this thesis are presented. First, we created Escritorie, a digital desk prototype based on the pen computer paradigm for transcribing handwritten text images. Second, we developed "Gestures à Go Go", a web application for bootstrapping gestures. Finally, we studied another interactive application under the pen computer paradigm. In this case, we study how translation reviewing can be done more ergonomically using a pen.[ES] Hoy en día, los ordenadores están presentes en todas partes pero su potencial no se aprovecha debido al "miedo" que se les tiene. En esta tesis se adopta el paradigma del pen computer, cuya idea fundamental es sustituir todos los dispositivos de entrada por un lápiz electrónico o, directamente, por los dedos. El origen del rechazo a los ordenadores proviene del uso de interfaces poco amigables para el humano. El origen de este paradigma data de hace más de 40 años, pero solo recientemente se ha comenzado a implementar en dispositivos móviles. La lenta y tardía implantación probablemente se deba a que es necesario incluir un reconocedor que "traduzca" los trazos del usuario (texto manuscrito o gestos) a algo entendible por el ordenador. Para pensar de forma realista en la implantación del pen computer, es necesario mejorar la precisión del reconocimiento de texto y gestos. El objetivo de esta tesis es el estudio de diferentes estrategias para mejorar esta precisión. En primer lugar, esta tesis investiga como aprovechar información derivada de la interacción para mejorar el reconocimiento, en concreto, en la transcripción interactiva de imágenes con texto manuscrito. En la transcripción interactiva, el sistema y el usuario trabajan "codo con codo" para generar la transcripción. El usuario valida la salida del sistema proporcionando ciertas correcciones, mediante texto manuscrito, que el sistema debe tener en cuenta para proporcionar una mejor transcripción. Este texto manuscrito debe ser reconocido para ser utilizado. En esta tesis se propone aprovechar información contextual, como por ejemplo, el prefijo validado por el usuario, para mejorar la calidad del reconocimiento de la interacción. Tras esto, la tesis se centra en el estudio del movimiento humano, en particular del movimiento de las manos, utilizando la Teoría Cinemática y su modelo Sigma-Lognormal. Entender como se mueven las manos al escribir, y en particular, entender el origen de la variabilidad de la escritura, es importante para el desarrollo de un sistema de reconocimiento, La contribución de esta tesis a este tópico es importante, dado que se presenta una nueva técnica (que mejora los resultados previos) para extraer el modelo Sigma-Lognormal de trazos manuscritos. De forma muy relacionada con el trabajo anterior, se estudia el beneficio de utilizar datos sintéticos como entrenamiento. La forma más fácil de entrenar un reconocedor es proporcionar un conjunto de datos "infinito" que representen todas las posibles variaciones. En general, cuanto más datos de entrenamiento, menor será el error del reconocedor. No obstante, muchas veces no es posible proporcionar más datos, o hacerlo es muy caro. Por ello, se ha estudiado como crear y usar datos sintéticos que se parezcan a los reales. Las diferentes contribuciones de esta tesis han obtenido buenos resultados, produciendo varias publicaciones en conferencias internacionales y revistas. Finalmente, también se han explorado tres aplicaciones relaciones con el trabajo de esta tesis. En primer lugar, se ha creado Escritorie, un prototipo de mesa digital basada en el paradigma del pen computer para realizar transcripción interactiva de documentos manuscritos. En segundo lugar, se ha desarrollado "Gestures à Go Go", una aplicación web para generar datos sintéticos y empaquetarlos con un reconocedor de forma rápida y sencilla. Por último, se presenta un sistema interactivo real bajo el paradigma del pen computer. En este caso, se estudia como la revisión de traducciones automáticas se puede realizar de forma más ergonómica.[CA] Avui en dia, els ordinadors són presents a tot arreu i es comunament acceptat que la seva utilització proporciona beneficis. No obstant això, moltes vegades el seu potencial no s'aprofita totalment. En aquesta tesi s'adopta el paradigma del pen computer, on la idea fonamental és substituir tots els dispositius d'entrada per un llapis electrònic, o, directament, pels dits. Aquest paradigma postula que l'origen del rebuig als ordinadors prové de l'ús d'interfícies poc amigables per a l'humà, que han de ser substituïdes per alguna cosa més coneguda. Per tant, la interacció amb l'ordinador sota aquest paradigma es realitza per mitjà de text manuscrit i/o gestos. L'origen d'aquest paradigma data de fa més de 40 anys, però només recentment s'ha començat a implementar en dispositius mòbils. La lenta i tardana implantació probablement es degui al fet que és necessari incloure un reconeixedor que "tradueixi" els traços de l'usuari (text manuscrit o gestos) a alguna cosa comprensible per l'ordinador, i el resultat d'aquest reconeixement, actualment, és lluny de ser òptim. Per pensar de forma realista en la implantació del pen computer, cal millorar la precisió del reconeixement de text i gestos. L'objectiu d'aquesta tesi és l'estudi de diferents estratègies per millorar aquesta precisió. En primer lloc, aquesta tesi investiga com aprofitar informació derivada de la interacció per millorar el reconeixement, en concret, en la transcripció interactiva d'imatges amb text manuscrit. En la transcripció interactiva, el sistema i l'usuari treballen "braç a braç" per generar la transcripció. L'usuari valida la sortida del sistema donant certes correccions, que el sistema ha d'usar per millorar la transcripció. En aquesta tesi es proposa utilitzar correccions manuscrites, que el sistema ha de reconèixer primer. La qualitat del reconeixement d'aquesta interacció és millorada, tenint en compte informació contextual, com per exemple, el prefix validat per l'usuari. Després d'això, la tesi se centra en l'estudi del moviment humà en particular del moviment de les mans, des del punt de vista generatiu, utilitzant la Teoria Cinemàtica i el model Sigma-Lognormal. Entendre com es mouen les mans en escriure és important per al desenvolupament d'un sistema de reconeixement, en particular, per entendre l'origen de la variabilitat de l'escriptura. La contribució d'aquesta tesi a aquest tòpic és important, atès que es presenta una nova tècnica (que millora els resultats previs) per extreure el model Sigma- Lognormal de traços manuscrits. De forma molt relacionada amb el treball anterior, s'estudia el benefici d'utilitzar dades sintètiques per a l'entrenament. La forma més fàcil d'entrenar un reconeixedor és proporcionar un conjunt de dades "infinit" que representin totes les possibles variacions. En general, com més dades d'entrenament, menor serà l'error del reconeixedor. No obstant això, moltes vegades no és possible proporcionar més dades, o fer-ho és molt car. Per això, s'ha estudiat com crear i utilitzar dades sintètiques que s'assemblin a les reals. Les diferents contribucions d'aquesta tesi han obtingut bons resultats, produint diverses publicacions en conferències internacionals i revistes. Finalment, també s'han explorat tres aplicacions relacionades amb el treball d'aquesta tesi. En primer lloc, s'ha creat Escritorie, un prototip de taula digital basada en el paradigma del pen computer per realitzar transcripció interactiva de documents manuscrits. En segon lloc, s'ha desenvolupat "Gestures à Go Go", una aplicació web per a generar dades sintètiques i empaquetar-les amb un reconeixedor de forma ràpida i senzilla. Finalment, es presenta un altre sistema inter- actiu sota el paradigma del pen computer. En aquest cas, s'estudia com la revisió de traduccions automàtiques es pot realitzar de forma més ergonòmica.Martín-Albo Simón, D. (2016). Contributions to Pen & Touch Human-Computer Interaction [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/68482TESI
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