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Multilingual phone recognition in Indian languages

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Multilingual phone recognition in Indian languagesby Manjunath K. E.
Author:	K. E., Manjunath.
Published:	Cham :Springer International Publishing :2022.
Description:	xiv, 103 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Automatic speech recognition.
Online resource:	https://doi.org/10.1007/978-3-030-80741-2
ISBN:	9783030807412$q(electronic bk.)

Multilingual phone recognition in Indian languages
K. E., Manjunath.

Multilingual phone recognition in Indian languages[electronic resource] /by Manjunath K. E. - Cham :Springer International Publishing :2022. - xiv, 103 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in speech technology,2191-7388. - SpringerBriefs in speech technology..

1. Introduction -- 2. Literature review -- 3. Development and analysis of Multilingual Phone recognition system -- 4. Prediction of Multilingual Articulatory Features -- 5. Articulatory Features of Multilingual Phone recognition -- 6. Applications of Multilingual Phone recognition in Code-switched and Non-code-switched Scenarios -- 7. Summary and Conclusion.

The book presents current research and developments in multilingual speech recognition. The author presents a Multilingual Phone Recognition System (Multi-PRS), developed using a common multilingual phone-set derived from the International Phonetic Alphabets (IPA) based transcription of six Indian languages - Kannada, Telugu, Bengali, Odia, Urdu, and Assamese. The author shows how the performance of Multi-PRS can be improved using tandem features. The book compares Monolingual Phone Recognition Systems (Mono-PRS) versus Multi-PRS and baseline versus tandem system. Methods are proposed to predict Articulatory Features (AFs) from spectral features using Deep Neural Networks (DNN) Multitask learning is explored to improve the prediction accuracy of AFs. Then, the AFs are explored to improve the performance of Multi-PRS using lattice rescoring method of combination and tandem method of combination. The author goes on to develop and evaluate the Language Identification followed by Monolingual phone recognition (LID-Mono) and common multilingual phone-set based multilingual phone recognition systems.

ISBN: 9783030807412$q(electronic bk.)

Standard No.: 10.1007/978-3-030-80741-2doiSubjects--Topical Terms:

184258
Automatic speech recognition.

LC Class. No.: TK7895.S65 / K4 2022

Dewey Class. No.: 006.454

Multilingual phone recognition in Indian languages
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100 1 $a K. E., Manjunath. $3 917529
245 1 0 $a Multilingual phone recognition in Indian languages $h [electronic resource] / $c by Manjunath K. E.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a xiv, 103 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a SpringerBriefs in speech technology, $x 2191-7388
505 0 $a 1. Introduction -- 2. Literature review -- 3. Development and analysis of Multilingual Phone recognition system -- 4. Prediction of Multilingual Articulatory Features -- 5. Articulatory Features of Multilingual Phone recognition -- 6. Applications of Multilingual Phone recognition in Code-switched and Non-code-switched Scenarios -- 7. Summary and Conclusion.
520 $a The book presents current research and developments in multilingual speech recognition. The author presents a Multilingual Phone Recognition System (Multi-PRS), developed using a common multilingual phone-set derived from the International Phonetic Alphabets (IPA) based transcription of six Indian languages - Kannada, Telugu, Bengali, Odia, Urdu, and Assamese. The author shows how the performance of Multi-PRS can be improved using tandem features. The book compares Monolingual Phone Recognition Systems (Mono-PRS) versus Multi-PRS and baseline versus tandem system. Methods are proposed to predict Articulatory Features (AFs) from spectral features using Deep Neural Networks (DNN) Multitask learning is explored to improve the prediction accuracy of AFs. Then, the AFs are explored to improve the performance of Multi-PRS using lattice rescoring method of combination and tandem method of combination. The author goes on to develop and evaluate the Language Identification followed by Monolingual phone recognition (LID-Mono) and common multilingual phone-set based multilingual phone recognition systems.
650 0 $a Automatic speech recognition. $3 184258
650 0 $a Computational linguistics $z India. $3 917530
650 1 4 $a Signal, Image and Speech Processing. $3 273768
650 2 4 $a Natural Language Processing (NLP) $3 826373
650 2 4 $a Computational Linguistics. $3 274669
710 2 $a SpringerLink (Online service) $3 273601
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