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Multilingual Phone Recognition in Indian Languages

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Multilingual Phone Recognition in Indian Languages/ by K.E Manjunath.
Author:	Manjunath, K.E.
Description:	XIV, 103 p. 28 illus., 9 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Speech processing systems. -
Online resource:	https://doi.org/10.1007/978-3-030-80741-2
ISBN:	9783030807412

Multilingual Phone Recognition in Indian Languages
Manjunath, K.E.

Multilingual Phone Recognition in Indian Languages[electronic resource] /by K.E Manjunath. - 1st ed. 2022. - XIV, 103 p. 28 illus., 9 illus. in color.online resource. - SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning,2191-7388. - SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning,.

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

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

564428
Speech processing systems.

LC Class. No.: TK7882.S65

Dewey Class. No.: 621.382

Multilingual Phone Recognition in Indian Languages
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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.
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