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Books on the topic 'Automatic speech recognition – Statistical methods'

Author: Grafiati

Published: 4 June 2021

Last updated: 30 January 2023

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1

Statistical methods for speech recognition. Cambridge, Mass: MIT Press, 1997.

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2

Alumäe, Tanel. Methods for Estonian large vocabulary speech recognition. [Tallinn]: Tallinn University of Technology Press, 2006.

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3

Joseph, Keshet, and Bengio Samy, eds. Automatic speech and speaker recognition: Large margin and kernel methods. Hoboken, NJ: J. Wiley & Sons, 2009.

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4

Minker, Wolfgang. Incorporating Knowledge Sources into Statistical Speech Recognition. Boston, MA: Springer Science+Business Media, LLC, 2009.

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5

Jelinek, Frederick. Statistical Methods for Speech Recognition. MIT Press, 2022.

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6

Keshet, Joseph, and Samy Bengio. Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods. Wiley & Sons, Limited, John, 2009.

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7

Keshet, Joseph, and Samy Bengio. Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods. Wiley & Sons, Incorporated, John, 2009.

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8

A, Fourcin, ed. Speech input and output assessment: Multilingual methods and standards. Chichester, West Sussex, England: E. Horwood, 1989.

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9

Müller, Christian. Speaker Classification I: Fundamentals, Features, and Methods. Springer London, Limited, 2007.

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10

Lamel, Lori, and Jean-Luc Gauvain. Speech Recognition. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0016.

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Speech recognition is concerned with converting the speech waveform, an acoustic signal, into a sequence of words. Today's approaches are based on a statistical modellization of the speech signal. This article provides an overview of the main topics addressed in speech recognition, which are, acoustic-phonetic modelling, lexical representation, language modelling, decoding, and model adaptation. Language models are used in speech recognition to estimate the probability of word sequences. The main components of a generic speech recognition system are, main knowledge sources, feature analysis, a

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11

Martín-Vide, Carlos, Luis Espinosa-Anke, and Irena Spasić. Statistical Language and Speech Processing: 9th International Conference, SLSP 2021, Cardiff, UK, November 23-25, 2021, Proceedings. Springer International Publishing AG, 2021.

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12

Statistical Language and Speech Processing: Second International Conference, SLSP 2014, Grenoble, France, October 14-16, 2014, Proceedings. Springer, 2014.

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13

Martín-Vide, Carlos, Matthew Purver, and Senja Pollak. Statistical Language and Speech Processing: 7th International Conference, SLSP 2019, Ljubljana, Slovenia, October 14–16, 2019, Proceedings. Springer, 2019.

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14

Dediu, Adrian-Horia, Carlos Martín-Vide, and Klára Vicsi. Statistical Language and Speech Processing: Third International Conference, SLSP 2015, Budapest, Hungary, November 24-26, 2015, Proceedings. Springer, 2015.

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15

Dediu, Adrian-Horia, Carlos Martín-Vide, and Laurent Besacier. Statistical Language and Speech Processing: Second International Conference, SLSP 2014, Grenoble, France, October 14-16, 2014, Proceedings. Springer, 2014.

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16

Dediu, Adrian-Horia, Carlos Martín-Vide, and Klára Vicsi. Statistical Language and Speech Processing: Third International Conference, SLSP 2015, Budapest, Hungary, November 24-26, 2015, Proceedings. Springer, 2015.

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17

Dutoit, Thierry, Carlos Martín-Vide, and Gueorgui Pironkov. Statistical Language and Speech Processing: 6th International Conference, SLSP 2018, Mons, Belgium, October 15–16, 2018, Proceedings. Springer, 2018.

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18

Mitkov, Ruslan, Carlos Martín-Vide, Bianca Truthe, and Adrian Horia Dediu. Statistical Language and Speech Processing: First International Conference, SLSP 2013, Tarragona, Spain, July 29-31, 2013, Proceedings. Springer, 2013.

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19

Martín-Vide, Carlos, Luis Espinosa-Anke, and Irena Spasić. Statistical Language and Speech Processing: 8th International Conference, SLSP 2020, Cardiff, UK, October 14-16, 2020, Proceedings. Springer International Publishing AG, 2020.

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20

Mitkov, Ruslan, Adrian-Horia Dediu, Carlos Martín-Vide, and Bianca Truthe. Statistical Language and Speech Processing: First International Conference, SLSP 2013, Tarragona, Spain, July 29-31, 2013, Proceedings. Springer, 2013.

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21

Martín-Vide, Carlos, Nathalie Camelin, and Yannick Estève. Statistical Language and Speech Processing: 5th International Conference, SLSP 2017, Le Mans, France, October 23–25, 2017, Proceedings. Springer, 2017.

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22

Martín-Vide, Carlos, and Pavel Král. Statistical Language and Speech Processing: 4th International Conference, SLSP 2016, Pilsen, Czech Republic, October 11-12, 2016, Proceedings. Springer, 2016.

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23

Datadriven Methods For Adaptive Spoken Dialogue Systems Computational Learning For Conversational Interfaces. Springer, 2012.

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24

Crespo Miguel, Mario. Automatic corpus-based translation of a spanish framenet medical glossary. 2020th ed. Editorial Universidad de Sevilla, 2020. http://dx.doi.org/10.12795/9788447230051.

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Computational linguistics is the scientific study of language from a computational perspective. It aims is to provide computational models of natural language processing (NLP) and incorporate them into practical applications such as speech synthesis, speech recognition, automatic translation and many others where automatic processing of language is required. The use of good linguistic resources is crucial for the development of computational linguistics systems. Real world applications need resources which systematize the way linguistic information is structured in a certain language. There is

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25

Little, Max A. Machine Learning for Signal Processing. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198714934.001.0001.

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Digital signal processing (DSP) is one of the ‘foundational’ engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relative newcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance and autonomous car navigation. Statistical machine learning exploits the analogy between intelli

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