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Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 30 stycznia 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, i Bengio Samy, red. 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, i Samy Bengio. Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods. Wiley & Sons, Limited, John, 2009.

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7

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

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8

A, Fourcin, red. 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, i Jean-Luc Gauvain. Speech Recognition. Redaktor Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0016.

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Streszczenie:

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, and acoustic and language models, which are estimated in a training phase, and the decoder. The focus of this article is on methods used in state-of-the-art speaker-independent, large-vocabulary continuous speech recognition (LVCSR). Primary application areas for such technology are dictation, spoken language dialogue, and transcription for information archival and retrieval systems. Finally, this article discusses issues and directions of future research.

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11

Martín-Vide, Carlos, Luis Espinosa-Anke i 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 i 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 i 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 i 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 i 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 i 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 i 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 i 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 i 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 i 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, i 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. Wyd. 2020. 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 a continuous effort to increase the number of linguistic resources available for the linguistic and NLP Community. Most of the existing linguistic resources have been created for English, mainly because most modern approaches to computational lexical semantics emerged in the United States. This situation is changing over time and some of these projects have been subsequently extended to other languages; however, in all cases, much time and effort need to be invested in creating such resources. Because of this, one of the main purposes of this work is to investigate the possibility of extending these resources to other languages such as Spanish. In this work, we introduce some of the most important resources devoted to lexical semantics, such as WordNet or FrameNet, and those focusing on Spanish such as 3LB-LEX or Adesse. Of these, this project focuses on FrameNet. The project aims to document the range of semantic and syntactic combinatory possibilities of words in English. Words are grouped according to the different frames or situations evoked by their meaning. If we focus on a particular topic domain like medicine and we try to describe it in terms of FrameNet, we probably would obtain frames representing it like CURE, formed by words like cure.v, heal.v or palliative.a or MEDICAL CONDITIONS with lexical units such as arthritis.n, asphyxia.n or asthma.n. The purpose of this work is to develop an automatic means of selecting frames from a particular domain and to translate them into Spanish. As we have stated, we will focus on medicine. The selection of the medical frames will be corpus-based, that is, we will extract all the frames that are statistically significant from a representative corpus. We will discuss why using a corpus-based approach is a reliable and unbiased way of dealing with this task. We will present an automatic method for the selection of FrameNet frames and, in order to make sure that the results obtained are coherent, we will contrast them with a previous manual selection or benchmark. Outcomes will be analysed by using the F-score, a measure widely used in this type of applications. We obtained a 0.87 F-score according to our benchmark, which demonstrates the applicability of this type of automatic approaches. The second part of the book is devoted to the translation of this selection into Spanish. The translation will be made using EuroWordNet, a extension of the Princeton WordNet for some European languages. We will explore different ways to link the different units of our medical FrameNet selection to a certain WordNet synset or set of words that have similar meanings. Matching the frame units to a specific synset in EuroWordNet allows us both to translate them into Spanish and to add new terms provided by WordNet into FrameNet. The results show how translation can be done quite accurately (95.6%). We hope this work can add new insight into the field of natural language processing.

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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 intelligent information processing in biological brains and sophisticated statistical modelling and inference. DSP and statistical machine learning are of such wide importance to the knowledge economy that both have undergone rapid changes and seen radical improvements in scope and applicability. Both make use of key topics in applied mathematics such as probability and statistics, algebra, calculus, graphs and networks. Intimate formal links between the two subjects exist and because of this many overlaps exist between the two subjects that can be exploited to produce new DSP tools of surprising utility, highly suited to the contemporary world of pervasive digital sensors and high-powered and yet cheap, computing hardware. This book gives a solid mathematical foundation to, and details the key concepts and algorithms in, this important topic.

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