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Punjabi language is popular Indo-Aryan language. Its phoneme sounds are tonal in nature which dissent in almost all-Indian side of Punjab. This books focus on analysis of some pf the dominant feature extraction techniques used in Automatic Speech Recognition and analytically analyse which feature extraction techniques is best suitable for extracting features of the tone present in the Punjabi speech. Three feature extractions techniques are compared: "power normalized cepstral coefficients (PNCC)", "Mel frequency cepstral coefficients (MFCC)" and "Perceptual Linear Prediction (PLP)" following a statistical comparison based on the accuracy and correctness of results attained. To attain a higher rate of accuracy level 34 phones for Punjabi language are used to break each word into small sound frames. environments using evident number of speakers giving overall system results with MFCC as finest of all three in noise-free environment and PLP to be efficient feature extraction technique in noisy environment for Punjabi speech corpus.
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Über die Autorin bzw. den Autor
Amitoj Singh is Doctorate in computer science and working as Assistant Professor in the department of computational Sciences, MRSPTU, Punjab India. With over 12 years f experience, he has filed 04 Patent, published 32 national/International papers, 04 books, 02 monographs, presented 15 papers in conferences, and sponsored projects to his credit.
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Singh, A: Feature-based Robust Techniques for Speech Recogni
Verlag:
LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6200673349
ISBN 13: 9786200673343
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Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Punjabi language is popular Indo-Aryan language. Its phoneme sounds are tonal in nature which dissent in almost all-Indian side of Punjab. This books focus on analysis of some pf the dominant feature extraction techniques used in Automatic Speech Recognition and analytically analyse which feature extraction techniques is best suitable for extracting features of the tone present in the Punjabi speech. Three feature extractions techniques are compared: 'power normalized cepstral coefficients (PNCC)', 'Mel frequency cepstral coefficients (MFCC)' and 'Perceptual Linear Prediction (PLP)' following a statistical comparison based on the accuracy and correctness of results attained. To attain a higher rate of accuracy level 34 phones for Punjabi language are used to break each word into small sound frames. environments using evident number of speakers giving overall system results with MFCC as finest of all three in noise-free environment and PLP to be efficient feature extraction technique in noisy environment for Punjabi speech corpus. Bestandsnummer des Verkäufers 9786200673343
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Feature-based Robust Techniques for Speech Recognition System
Verlag:
LAP LAMBERT Academic Publishing Okt 2025, 2025
ISBN 10: 6200673349
ISBN 13: 9786200673343
Neu
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Punjabi language is popular Indo-Aryan language. Its phoneme sounds are tonal in nature which dissent in almost all-Indian side of Punjab. This books focus on analysis of some pf the dominant feature extraction techniques used in Automatic Speech Recognition and analytically analyse which feature extraction techniques is best suitable for extracting features of the tone present in the Punjabi speech. Three feature extractions techniques are compared: 'power normalized cepstral coefficients (PNCC)', 'Mel frequency cepstral coefficients (MFCC)' and 'Perceptual Linear Prediction (PLP)' following a statistical comparison based on the accuracy and correctness of results attained. To attain a higher rate of accuracy level 34 phones for Punjabi language are used to break each word into small sound frames. environments using evident number of speakers giving overall system results with MFCC as finest of all three in noise-free environment and PLP to be efficient feature extraction technique in noisy environment for Punjabi speech corpus.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 60 pp. Englisch. Bestandsnummer des Verkäufers 9786200673343
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Feature-based Robust Techniques for Speech Recognition System : Second Edition
Verlag:
LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6200673349
ISBN 13: 9786200673343
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Punjabi language is popular Indo-Aryan language. Its phoneme sounds are tonal in nature which dissent in almost all-Indian side of Punjab. This books focus on analysis of some pf the dominant feature extraction techniques used in Automatic Speech Recognition and analytically analyse which feature extraction techniques is best suitable for extracting features of the tone present in the Punjabi speech. Three feature extractions techniques are compared: 'power normalized cepstral coefficients (PNCC)', 'Mel frequency cepstral coefficients (MFCC)' and 'Perceptual Linear Prediction (PLP)' following a statistical comparison based on the accuracy and correctness of results attained. To attain a higher rate of accuracy level 34 phones for Punjabi language are used to break each word into small sound frames. environments using evident number of speakers giving overall system results with MFCC as finest of all three in noise-free environment and PLP to be efficient feature extraction technique in noisy environment for Punjabi speech corpus. Bestandsnummer des Verkäufers 9786200673343
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Feature-based Robust Techniques for Speech Recognition System | - Second Edition
Verlag:
LAP LAMBERT Academic Publishing, 2025
ISBN 10: 6200673349
ISBN 13: 9786200673343
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Taschenbuch
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Taschenbuch. Zustand: Neu. Feature-based Robust Techniques for Speech Recognition System | - Second Edition | Amitoj Singh (u. a.) | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786200673343 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. Bestandsnummer des Verkäufers 134197601
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