ISBN: 9786200531629 - approach to speech processing: speech recognition by feature extraction (5 resultados)

Taschenbuch. Condición: Neu. Approach to Speech Processing | Speech Recognition by Feature Extraction | Pramod Patil | Taschenbuch | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9786200531629 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Artificial neural network based speech recognition by feature extraction analysisSpeech recognition by machine is a critical core technology for the 'information' age. Existing machine recognition systems do not work the way human work. This is because automatic speech recognition (ASR) machines use spectral templates, while human work with partial recognition information across frequency, probably in the form of speech features that are local in frequency (e.g., formants). The forcing partial recognition errors in one frequency region do not affect the partial recognition at other frequencies (i.e., the partial recognition errors across frequency are independent). To extract the features spread across frequency requires frequency-local signal processing. Although a great deal has been learned, the fundamental process of speech production and speech perception, the goal of recognition of fluent speech remains elusive. The fundamental function of a speech recognition system is to identify trial speech utterances belonging to a given vocabulary with the highest possible degree, while rejecting those utterances that do not belong to the vocabulary. 104 pp. Englisch.…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Artificial neural network based speech recognition by feature extraction analysisSpeech recognition by machine is a critical core technology for the 'information' age. Existing machine recognition systems do not work the way human work. This is because automatic speech recognition (ASR) machines use spectral templates, while human work with partial recognition information across frequency, probably in the form of speech features that are local in frequency (e.g., formants). The forcing partial recognition errors in one frequency region do not affect the partial recognition at other frequencies (i.e., the partial recognition errors across frequency are independent). To extract the features spread across frequency requires frequency-local signal processing. Although a great deal has been learned, the fundamental process of speech production and speech perception, the goal of recognition of fluent speech remains elusive. The fundamental function of a speech recognition system is to identify trial speech utterances belonging to a given vocabulary with the highest possible degree, while rejecting those utterances that do not belong to the vocabulary.…

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Patil PramodElectronics Engineer (BE, ME, PhD) working as Principal at Engineering college since last 12 years out of 30 years of teaching experience. Registered 6 patents and supervised 4 Doctoral and 13 Masters students. Published .…

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Artificial neural network based speech recognition by feature extraction analysisSpeech recognition by machine is a critical core technology for the 'information' age. Existing machine recognition systems do not work the way human work. This is because automatic speech recognition (ASR) machines use spectral templates, while human work with partial recognition information across frequency, probably in the form of speech features that are local in frequency (e.g., formants). The forcing partial recognition errors in one frequency region do not affect the partial recognition at other frequencies (i.e., the partial recognition errors across frequency are independent). To extract the features spread across frequency requires frequency-local signal processing. Although a great deal has been learned, the fundamental process of speech production and speech perception, the goal of recognition of fluent speech remains elusive. The fundamental function of a speech recognition system is to identify trial speech utterances belonging to a given vocabulary with the highest possible degree, while rejecting those utterances that do not belong to the vocabulary.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 104 pp. Englisch.…