9786204738581 - Detection of Sigmatism with the Aid of Machine Learning: for German Speakers by Barabashova, Kristina (7 results)

Condition: New.

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.

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.

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 %.

Condition: New. Print on Demand.

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.

Condition: New. PRINT ON DEMAND.