253 research outputs found

    NIST 2007 Language Recognition Evaluation: From the Perspective of IIR

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    PACLIC / The University of the Philippines Visayas Cebu College Cebu City, Philippines / November 20-22, 200

    Applying feature reduction analysis to a PPRLM-multiple Gaussian language identification system

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    This paper presents the application of a feature selection technique such as LDA to a language identification (LID) system. The baseline system consists of a PPRLM module followed by a multiple-Gaussian classifier. This classifier makes use of acoustic scores and duration features of each input utterance. We applied a dimension reduction of the feature space in order to achieve a faster and easier-trainable system. We imputed missing values of our vectors before projecting them on the new space. Our experiments show a very low performance reduction due to the dimension reduction approach. Using a single dimension projection the error rates we have obtained are about 8.73% taking into account the 22 most significant features

    Frame-level features conveying phonetic information for language and speaker recognition

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    150 p.This Thesis, developed in the Software Technologies Working Group of the Departmentof Electricity and Electronics of the University of the Basque Country, focuseson the research eld of spoken language and speaker recognition technologies.More specically, the research carried out studies the design of a set of featuresconveying spectral acoustic and phonotactic information, searches for the optimalfeature extraction parameters, and analyses the integration and usage of the featuresin language recognition systems, and the complementarity of these approacheswith regard to state-of-the-art systems. The study reveals that systems trained onthe proposed set of features, denoted as Phone Log-Likelihood Ratios (PLLRs), arehighly competitive, outperforming in several benchmarks other state-of-the-art systems.Moreover, PLLR-based systems also provide complementary information withregard to other phonotactic and acoustic approaches, which makes them suitable infusions to improve the overall performance of spoken language recognition systems.The usage of this features is also studied in speaker recognition tasks. In this context,the results attained by the approaches based on PLLR features are not as remarkableas the ones of systems based on standard acoustic features, but they still providecomplementary information that can be used to enhance the overall performance ofthe speaker recognition systems

    Acoustic model selection for recognition of regional accented speech

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    Accent is cited as an issue for speech recognition systems. Our experiments showed that the ASR word error rate is up to seven times greater for accented speech compared with standard British English. The main objective of this research is to develop Automatic Speech Recognition (ASR) techniques that are robust to accent variation. We applied different acoustic modelling techniques to compensate for the effects of regional accents on the ASR performance. For conventional GMM-HMM based ASR systems, we showed that using a small amount of data from a test speaker to choose an accent dependent model using an accent identification system, or building a model using the data from N neighbouring speakers in AID space, will result in superior performance compared to that obtained with unsupervised or supervised speaker adaptation. In addition we showed that using a DNN-HMM rather than a GMM-HMM based acoustic model would improve the recognition accuracy considerably. Even if we apply two stages of accent followed by speaker adaptation to the GMM-HMM baseline system, the GMM-HMM based system will not outperform the baseline DNN-HMM based system. For more contemporary DNN-HMM based ASR systems we investigated how adding different types of accented data to the training set can provide better recognition accuracy on accented speech. Finally, we proposed a new approach for visualisation of the AID feature space. This is helpful in analysing the AID recognition accuracies and analysing AID confusion matrices

    Reconocimiento automático de locutor e idioma mediante caracterización acústica de unidades lingüísticas

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    Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones . Fecha de lectura: 30-06-201

    Loan Phonology

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    For many different reasons, speakers borrow words from other languages to fill gaps in their own lexical inventory. The past ten years have been characterized by a great interest among phonologists in the issue of how the nativization of loanwords occurs. The general feeling is that loanword nativization provides a direct window for observing how acoustic cues are categorized in terms of the distinctive features relevant to the L1 phonological system as well as for studying L1 phonological processes in action and thus to the true synchronic phonology of L1. The collection of essays presented in this volume provides an overview of the complex issues phonologists face when investigating this phenomenon and, more generally, the ways in which unfamiliar sounds and sound sequences are adapted to converge with the native language’s sound pattern. This book is of interest to theoretical phonologists as well as to linguists interested in language contact phenomena
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