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Big Data analytics to assess personality based on voice analysis
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Reconocimiento de voz basado en características DNN Bottleneck
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Implementación y evaluación de un sistema QbE-STD (Query-by-Example Spoken Term Detection)
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An analysis of the influence of deep neural network (DNN) topology in bottleneck feature based language recognition
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ALBAYZIN Query-by-example Spoken Term Detection 2016 evaluation
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An end-to-end approach to language identification in short utterances using convolutional neural networks
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Emulating DNA: Rigorous Quantification of Evidential Weight in Transparent and Testable Forensic Speaker Recognition
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In: IEEE Transactions on Audio, Speech, and Language Processing (2015)
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Emulating DNA: Rigorous Quantification of Evidential Weight in Transparent and Testable Forensic Speaker Recognition
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In: IEEE Transactions on Audio, Speech, and Language Processing (2015)
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Speech Signal and Facial Image Processing for Obstructive Sleep Apnea Assessment
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Acoustic-phonetic decoding of different types of spontaneous speech in Spanish
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Severe apnoea detection using speaker recognition techniques
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Using data-driven and phonetic units for speaker verification
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Multivariate cepstral feature compensation on band-limited data for robust speech recognition
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Acoustic Event Recognition for Low Cost Language Identification
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On the relationship between phonetic modeling precision and phonetic speaker recognition accuracy
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Inventario de frecuencias fonémicas y silábicas del castellano espontáneo y escrito
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Improved language recognition using better phonetic decoders and fusion with MFCC and SDC features
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Feature analysis for discriminative confidence estimation in spoken term detection
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Abstract:
This is the author’s version of a work that was accepted for publication in Computer Speech & Language. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Speech & Language, 28, 5, (2014) DOI:10.1016/j.csl.2013.09.008 ; Discriminative confidence based on multi-layer perceptrons (MLPs) and multiple features has shown significant advantage compared to the widely used lattice-based confidence in spoken term detection (STD). Although the MLP-based framework can handle any features derived from a multitude of sources, choosing all possible features may lead to over complex models and hence less generality. In this paper, we design an extensive set of features and analyze their contribution to STD individually and as a group. The main goal is to choose a small set of features that are sufficiently informative while keeping the model simple and generalizable. We employ two established models to conduct the analysis: one is linear regression which targets for the most relevant features and the other is logistic linear regression which targets for the most discriminative features. We find the most informative features are comprised of those derived from diverse sources (ASR decoding, duration and lexical properties) and the two models deliver highly consistent feature ranks. STD experiments on both English and Spanish data demonstrate significant performance gains with the proposed feature sets. ; This work has been partially supported by project PriorSPEECH (TEC2009-14719-C02-01) from the Spanish Ministry of Science and Innovation and by project MAV2VICMR (S2009/TIC-1542) from the Community of Madrid.
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Keyword:
Discriminative confidence; Feature analysis; Speech recognition; Spoken term detection; Telecomunicaciones
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URL: https://doi.org/10.1016/j.csl.2013.09.008 http://hdl.handle.net/10486/662781
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Assessment of severe apnoea through voice analysis, automatic speech, and speaker recognition techniques
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