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Language modeling for automatic speech recognition of inflective languages [electronic resource] : an applications-oriented approach using lexical data

Language modeling for automatic speech recognition of inflective languages [electronic resource] : an applications-oriented approach using lexical data

자료유형
E-Book(소장)
개인저자
Donaj, Gregor. Kačič, Zdravko.
서명 / 저자사항
Language modeling for automatic speech recognition of inflective languages [electronic resource] : an applications-oriented approach using lexical data / Gregor Donaj, Zdravko Kačič.
발행사항
Cham :   Springer,   2017.  
형태사항
1 online resource (viii, 71 p.) : ill. (some col.).
총서사항
SpringerBriefs in Electrical and Computer Engineering,2191-8112, 2191-8120 (electronic)
ISBN
9783319416052 9783319416076 (eBook)
요약
This book covers language modeling and automatic speech recognition for inflective languages (e.g. Slavic languages), which represent roughly half of the languages spoken in Europe. These languages do not perform as well as English in speech recognition systems and it is therefore harder to develop an application with sufficient quality for the end user. The authors describe the most important language features for the development of a speech recognition system. This is then presented through the analysis of errors in the system and the development of language models and their inclusion in speech recognition systems, which specifically address the errors that are relevant for targeted applications. The error analysis is done with regard to morphological characteristics of the word in the recognized sentences. The book is oriented towards speech recognition with large vocabularies and continuous and even spontaneous speech. Today such applications work with a rather small number of languages compared to the number of spoken languages. Concentrates on speech recognition for inflective languages – representative of roughly half of Europe -- and their unique characteristics Introduces new application-oriented methods for measuring the performance of a speech recognition system Presents examples of language modeling to maximize the performance of a speech recognition system Provides techniques for analyzing errors and identifying their sources in a speech recognition system from a lexical point of view rather than acoustic point of view.
일반주기
Title from e-Book title page.  
내용주기
Introduction -- Speech Recognition in Inflective Languages -- Performance Evaluation Using Lexical Data -- Application Oriented Language Modeling -- An Example Application -- Conclusion.
서지주기
Includes bibliographical references.
이용가능한 다른형태자료
Issued also as a book.  
일반주제명
Automatic speech recognition. Computational linguistics.
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245 1 0 ▼a Language modeling for automatic speech recognition of inflective languages ▼h [electronic resource] : ▼b an applications-oriented approach using lexical data / ▼c Gregor Donaj, Zdravko Kačič.
260 ▼a Cham : ▼b Springer, ▼c 2017.
300 ▼a 1 online resource (viii, 71 p.) : ▼b ill. (some col.).
490 1 ▼a SpringerBriefs in Electrical and Computer Engineering, ▼x 2191-8112, ▼x 2191-8120 (electronic)
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references.
505 0 ▼a Introduction -- Speech Recognition in Inflective Languages -- Performance Evaluation Using Lexical Data -- Application Oriented Language Modeling -- An Example Application -- Conclusion.
520 ▼a This book covers language modeling and automatic speech recognition for inflective languages (e.g. Slavic languages), which represent roughly half of the languages spoken in Europe. These languages do not perform as well as English in speech recognition systems and it is therefore harder to develop an application with sufficient quality for the end user. The authors describe the most important language features for the development of a speech recognition system. This is then presented through the analysis of errors in the system and the development of language models and their inclusion in speech recognition systems, which specifically address the errors that are relevant for targeted applications. The error analysis is done with regard to morphological characteristics of the word in the recognized sentences. The book is oriented towards speech recognition with large vocabularies and continuous and even spontaneous speech. Today such applications work with a rather small number of languages compared to the number of spoken languages. Concentrates on speech recognition for inflective languages – representative of roughly half of Europe -- and their unique characteristics Introduces new application-oriented methods for measuring the performance of a speech recognition system Presents examples of language modeling to maximize the performance of a speech recognition system Provides techniques for analyzing errors and identifying their sources in a speech recognition system from a lexical point of view rather than acoustic point of view.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
650 0 ▼a Automatic speech recognition.
650 0 ▼a Computational linguistics.
700 1 ▼a Kačič, Zdravko.
830 0 ▼a SpringerBriefs in Electrical and Computer Engineering.
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=https://doi.org/10.1007/978-3-319-41607-6
945 ▼a KLPA
991 ▼a E-Book(소장)

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/e-Book 컬렉션/ 청구기호 CR 006.454 등록번호 E14017579 도서상태 대출불가(열람가능) 반납예정일 예약 서비스 M

컨텐츠정보

책소개

This book covers language modeling and automatic speech recognition for inflective languages (e.g. Slavic languages), which represent roughly half of the languages spoken in Europe. These languages do not perform as well as English in speech recognition systems and it is therefore harder to develop an application with sufficient quality for the end user. The authors describe the most important language features for the development of a speech recognition system. This is then presented through the analysis of errors in the system and the development of language models and their inclusion in speech recognition systems, which specifically address the errors that are relevant for targeted applications. The error analysis is done with regard to morphological characteristics of the word in the recognized sentences. The book is oriented towards speech recognition with large vocabularies and continuous and even spontaneous speech. Today such applications work with a rather small number of languages compared to the number of spoken languages.

New feature

This book covers language modeling and automatic speech recognition for inflective languages (e.g. Slavic languages), which represent roughly half of the languages spoken in Europe. These languages do not perform as well as English in speech recognition systems and it is therefore harder to develop an application with sufficient quality for the end user. The authors describe the most important language features for the development of a speech recognition system. This is then presented through the analysis of errors in the system and the development of language models and their inclusion in speech recognition systems, which specifically address the errors that are relevant for targeted applications. The error analysis is done with regard to morphological characteristics of the word in the recognized sentences. The book is oriented towards speech recognition with large vocabularies and continuous and even spontaneous speech. Today such applications work with a rather small number of languages compared to the number of spoken languages.

Concentrates on speech recognition for inflective languages ? representative of roughly half of Europe -- and their unique characteristics

Introduces new application-oriented methods for measuring the performance of a speech recognition system

Presents examples of language modeling to maximize the performance of a speech recognition system

Provides techniques for analyzing errors and identifying their sources in a speech recognition system from a lexical point of view rather than acoustic point of view



정보제공 : Aladin

목차

Introduction.- Speech Recognition in Inflective Languages.- Performance Evaluation Using Lexical Data.- Application Oriented Language Modeling.- An Example Application.- Conclusion.


정보제공 : Aladin

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