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Speech recognition in a dialog system: from conventional to deep processing

Authors:

Aldonso Becerra,

J. Ismael De La Rosa,

Efrén GonzálezAuthors Info & Claims

Multimedia Tools and Applications, Volume 77, Issue 12

Pages 15875 - 15911

https://doi.org/10.1007/s11042-017-5160-5

Published: 01 June 2018 Publication History

Abstract

The aim of this paper is to illustrate an overview of the automatic speech recognition (ASR) module in a spoken dialog system and how it has evolved from the conventional GMM-HMM (Gaussian mixture model - hidden Markov model) architecture toward the recent nonlinear DNN-HMM (deep neural network) scheme. GMMs have dominated for a long time the baseline of speech recognition, but in the past years with the resurgence of artificial neural networks (ANNs), the former models have been surpassed in most recognition tasks. An outstanding consideration for ANNs-based acoustic model is the fact that their weights can be adjusted in two training steps: i) initialization of the weights (with or without pre-training) and ii) fine-tuning. To exemplify these frameworks, a case study is realized by using the Kaldi toolkit, employing a mid-vocabulary with a personalized speaker-independent voice corpus on a connected-words phone dialing environment operated for recognition of digit strings and personal name lists in Spanish from Mexico. The obtained results show a reasonable accuracy in the speech recognition performance through the DNN acoustic modeling. A word error rate (WER) of 1.49% for context-dependent DNN-HMM is achieved, providing a 30% relative improvement with regard to the best GMM-HMM result in these experiments (2.12% WER).

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Cited By

Becerra ARosa JVelásquez EZepeda GEscalante NPedroza A(2024)Portable student attendance management module for university environment by using biometric mechanismsMultimedia Tools and Applications10.1007/s11042-023-15482-y83:1(1215-1239)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15482-y
Kaur NSingh P(2022)Speech waveform reconstruction from speech parameters for an effective text to speech synthesis system using minimum phase harmonic sinusoidal model for PunjabiMultimedia Tools and Applications10.1007/s11042-022-12850-y81:18(26101-26120)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s11042-022-12850-y
Maity KPradhan GSingh J(2021)A Pitch and Noise Robust Keyword Spotting System Using SMAC Features with Prosody ModificationCircuits, Systems, and Signal Processing10.1007/s00034-020-01565-w40:4(1892-1904)Online publication date: 1-Apr-2021
https://dl.acm.org/doi/10.1007/s00034-020-01565-w
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cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 77, Issue 12

June 2018

1482 pages

ISSN:1380-7501

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Copyright © Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature.

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Kluwer Academic Publishers

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Published: 01 June 2018

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Cited By

Becerra ARosa JVelásquez EZepeda GEscalante NPedroza A(2024)Portable student attendance management module for university environment by using biometric mechanismsMultimedia Tools and Applications10.1007/s11042-023-15482-y83:1(1215-1239)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15482-y
Kaur NSingh P(2022)Speech waveform reconstruction from speech parameters for an effective text to speech synthesis system using minimum phase harmonic sinusoidal model for PunjabiMultimedia Tools and Applications10.1007/s11042-022-12850-y81:18(26101-26120)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s11042-022-12850-y
Maity KPradhan GSingh J(2021)A Pitch and Noise Robust Keyword Spotting System Using SMAC Features with Prosody ModificationCircuits, Systems, and Signal Processing10.1007/s00034-020-01565-w40:4(1892-1904)Online publication date: 1-Apr-2021
https://dl.acm.org/doi/10.1007/s00034-020-01565-w
Becerra ARosa JGonzález EPedroza AEscalante N(2018)Training deep neural networks with non-uniform frame-level cost function for automatic speech recognitionMultimedia Tools and Applications10.1007/s11042-018-5917-577:20(27231-27267)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1007/s11042-018-5917-5

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