Synopsis
The book is devoted to automatic detection of sigmatism in adult speech of German speakers. It has two major purposes: (1) to find an optimal set of audio features providing distinction between normal and disordered speech; (2) to create a Machine Learning (ML) classification algorithm able to analyze extracted features and detect sigmatism at phone level. The features are selected according to the phonetic background of considered sounds.They include first three formants, root-mean-square (RMS) amplitude, spectral peaks, spectral centroid, spectral skewness, and first 12 mel-frequency cepstral coefficients (MFCCs). Three ML methods are considered for sigmatism detection: Support Vector Machine, Gaussian Process, and Neural Networks. The process of feature extraction as well as automatic classification are conducted via Python scripts. As a result, the model based on SVM with the RBF kernel showed the highest accuracy rate of 90.6 %.
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Detection of Sigmatism with the aid of Machine Learning
Published by
LAP LAMBERT Academic Publishing Jan 2022, 2022
ISBN 10: 6204738585
ISBN 13: 9786204738581
New
Taschenbuch
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The book is devoted to automatic detection of sigmatism in adult speech of German speakers. It has two major purposes: (1) to find an optimal set of audio features providing distinction between normal and disordered speech; (2) to create a Machine Learning (ML) classification algorithm able to analyze extracted features and detect sigmatism at phone level. The features are selected according to the phonetic background of considered sounds.They include first three formants, root-mean-square (RMS) amplitude, spectral peaks, spectral centroid, spectral skewness, and first 12 mel-frequency cepstral coefficients (MFCCs). Three ML methods are considered for sigmatism detection: Support Vector Machine, Gaussian Process, and Neural Networks. The process of feature extraction as well as automatic classification are conducted via Python scripts. As a result, the model based on SVM with the RBF kernel showed the highest accuracy rate of 90.6 %. 80 pp. Englisch. Seller Inventory # 9786204738581
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Detection of Sigmatism with the aid of Machine Learning
Published by
LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6204738585
ISBN 13: 9786204738581
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Seller: moluna, Greven, Germany
Seller rating 4 out of 5 stars
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Barabashova KristinaMy name is Kristina. I am studying and working in the area of speech and language technologies. My personal goal is to acquire skills and knowledge that I can use for the public good, making the life of people mor. Seller Inventory # 560259895
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Detection of Sigmatism with the aid of Machine Learning
Published by
LAP LAMBERT Academic Publishing Jan 2022, 2022
ISBN 10: 6204738585
ISBN 13: 9786204738581
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Neuware -The book is devoted to automatic detection of sigmatism in adult speech of German speakers. It has two major purposes: (1) to find an optimal set of audio features providing distinction between normal and disordered speech; (2) to create a Machine Learning (ML) classification algorithm able to analyze extracted features and detect sigmatism at phone level. The features are selected according to the phonetic background of considered sounds.They include first three formants, root-mean-square (RMS) amplitude, spectral peaks, spectral centroid, spectral skewness, and first 12 mel-frequency cepstral coefficients (MFCCs). Three ML methods are considered for sigmatism detection: Support Vector Machine, Gaussian Process, and Neural Networks. The process of feature extraction as well as automatic classification are conducted via Python scripts. As a result, the model based on SVM with the RBF kernel showed the highest accuracy rate of 90.6 %.Books on Demand GmbH, Überseering 33, 22297 Hamburg 80 pp. Englisch. Seller Inventory # 9786204738581
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Detection of Sigmatism with the aid of Machine Learning : for German Speakers
Published by
LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6204738585
ISBN 13: 9786204738581
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The book is devoted to automatic detection of sigmatism in adult speech of German speakers. It has two major purposes: (1) to find an optimal set of audio features providing distinction between normal and disordered speech; (2) to create a Machine Learning (ML) classification algorithm able to analyze extracted features and detect sigmatism at phone level. The features are selected according to the phonetic background of considered sounds.They include first three formants, root-mean-square (RMS) amplitude, spectral peaks, spectral centroid, spectral skewness, and first 12 mel-frequency cepstral coefficients (MFCCs). Three ML methods are considered for sigmatism detection: Support Vector Machine, Gaussian Process, and Neural Networks. The process of feature extraction as well as automatic classification are conducted via Python scripts. As a result, the model based on SVM with the RBF kernel showed the highest accuracy rate of 90.6 %. Seller Inventory # 9786204738581
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Detection of Sigmatism with the aid of Machine Learning | for German Speakers
Published by
LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6204738585
ISBN 13: 9786204738581
New
Taschenbuch
Seller: preigu, Osnabrück, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Detection of Sigmatism with the aid of Machine Learning | for German Speakers | Kristina Barabashova | Taschenbuch | Englisch | 2022 | LAP LAMBERT Academic Publishing | EAN 9786204738581 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 121163731