Exploring the role of phonetic bottleneck features for speaker and language recognition

Using bottleneck features extracted from a deep neural network (DNN) trained to predict senone posteriors has resulted in new, state-of-the-art technology for language and speaker identification. For language identification, the features' dense phonetic information is believed to enable improve...

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Detalles Bibliográficos
Publicado: 2016
Materias:
Bottleneck Features
Deep Neural Networks
Language Recognition
Speaker Recognition
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_15206149_v2016-May_n_p5575_McLaren
http://hdl.handle.net/20.500.12110/paper_15206149_v2016-May_n_p5575_McLaren
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_15206149_v2016-May_n_p5575_McLaren
record_format dspace
spelling paper:paper_15206149_v2016-May_n_p5575_McLaren2023-06-08T16:19:13Z Exploring the role of phonetic bottleneck features for speaker and language recognition Bottleneck Features Deep Neural Networks Language Recognition Speaker Recognition Using bottleneck features extracted from a deep neural network (DNN) trained to predict senone posteriors has resulted in new, state-of-the-art technology for language and speaker identification. For language identification, the features' dense phonetic information is believed to enable improved performance by better representing language-dependent phone distributions. For speaker recognition, the role of these features is less clear, given that a bottleneck layer near the DNN output layer is thought to contain limited speaker information. In this article, we analyze the role of bottleneck features in these identification tasks by varying the DNN layer from which they are extracted, under the hypothesis that speaker information is traded for dense phonetic information as the layer moves toward the DNN output layer. Experiments support this hypothesis under certain conditions, and highlight the benefit of using a bottleneck layer close to the DNN output layer when DNN training data is matched to the evaluation conditions, and a layer more central to the DNN otherwise. © 2016 IEEE. 2016 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_15206149_v2016-May_n_p5575_McLaren http://hdl.handle.net/20.500.12110/paper_15206149_v2016-May_n_p5575_McLaren
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Bottleneck Features
Deep Neural Networks
Language Recognition
Speaker Recognition
spellingShingle Bottleneck Features
Deep Neural Networks
Language Recognition
Speaker Recognition
Exploring the role of phonetic bottleneck features for speaker and language recognition
topic_facet Bottleneck Features
Deep Neural Networks
Language Recognition
Speaker Recognition
description Using bottleneck features extracted from a deep neural network (DNN) trained to predict senone posteriors has resulted in new, state-of-the-art technology for language and speaker identification. For language identification, the features' dense phonetic information is believed to enable improved performance by better representing language-dependent phone distributions. For speaker recognition, the role of these features is less clear, given that a bottleneck layer near the DNN output layer is thought to contain limited speaker information. In this article, we analyze the role of bottleneck features in these identification tasks by varying the DNN layer from which they are extracted, under the hypothesis that speaker information is traded for dense phonetic information as the layer moves toward the DNN output layer. Experiments support this hypothesis under certain conditions, and highlight the benefit of using a bottleneck layer close to the DNN output layer when DNN training data is matched to the evaluation conditions, and a layer more central to the DNN otherwise. © 2016 IEEE.
title Exploring the role of phonetic bottleneck features for speaker and language recognition
title_short Exploring the role of phonetic bottleneck features for speaker and language recognition
title_full Exploring the role of phonetic bottleneck features for speaker and language recognition
title_fullStr Exploring the role of phonetic bottleneck features for speaker and language recognition
title_full_unstemmed Exploring the role of phonetic bottleneck features for speaker and language recognition
title_sort exploring the role of phonetic bottleneck features for speaker and language recognition
publishDate 2016
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_15206149_v2016-May_n_p5575_McLaren
http://hdl.handle.net/20.500.12110/paper_15206149_v2016-May_n_p5575_McLaren
_version_ 1768546644953923584

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