Global ETD Search

1	Robustní detekce klíčových slov v řečovém signálu / Robust detection of keywords in speech signal Vrba, Václav January 2014 (has links) The master thesis is divided into two parts theoretical and practical. The theoretical part is focused on methods of analysis and detection of speech signals. In the practical part the system for isolated word recognition was created in Matlab. The system is speaker independent separately for men and women. Also two speech databases were created for further use in the aircraft cockpit. Tests and evaluations were performed even with added noise.
2	A Design of Recognition Rate Improving Strategy for Mandarin Speech Recognition System - A Case Study on Address Inputting System and Phrase Recognition System Hsieh, Wen-kuang 24 August 2009 (has links) This thesis investigates the recognition rate improvement strategies for a Mandarin speech recognition system. Both automatic tone recognition and consonant correction schemes are studied and applied to the Mandarin address inputting system and the Mandarin 2, 3, 4-word phrase recognition systems. For automatic tone recognition scheme, the acoustic properties of the four tones in the Mandarin training database are estimated statistically by 4 sets of parameters within 6 minutes. These automatically generated parameters can greatly increase the tone recognition accuracy, and at the same time reduce the amount of time spent in the manual tone parameter adjustment, that is about 8 hours in general. For consonant correction scheme, the sub-syllable models are developed to enhance the consonant recognition accuracy, and hence further improve the overall correct rate for the whole Mandarin phrases. Experimental results indicate that over 90% correct rate can be achieved for the Mandarin address inputting system with 180 thousand place names by applying the above two schemes. Furthermore, the recognition rates for the Mandarin 2, 3, 4-word phrase recognition systems with 116 thousand phrases in total can be improved from 77%, 94% and 97.5%, to 85%, 96% and 98% respectively. speech recognition hidden Markov model sub-syllable model automatic tone recognition LPCC MFCC
3	Analýza Parkinsonovy nemoci pomocí segmentálních řečových příznaků / Analysis of Parkinson's disease using segmental speech parameters Mračko, Peter January 2015 (has links) This project describes design of the system for diagnosis Parkinson’s disease based on speech. Parkinson’s disease is a neurodegenerative disorder of the central nervous system. One of the symptoms of this disease is disability of motor aspects of speech, called hypokinetic dysarthria. Design of the system in this work is based on the best known segmental features such as coefficients LPC, PLP, MFCC, LPCC but also less known such as CMS, ACW and MSC. From speech records of patients affected by Parkinson’s disease and also healthy controls are calculated these coefficients, further is performed a selection process and subsequent classification. The best result, which was obtained in this project reached classification accuracy 77,19%, sensitivity 74,69% and specificity 78,95%.
4	Utilisation des signaux du cerveau (EEG) et vocaux pour la détection et le monitoring des facultés d'une personne Ben Messaoud, Aymen January 2020 (has links) (PDF) No description available. Alcool Auto Automobile Cannabis Cerveau Classification Communication Conduite Convolutif Convolutionnel Détection Dysarthrie EEG Électroencéphalographie Élocution Faculté FFT Français Humain LPC LPCC MEL MFCC Parole PLP Signal Son Véhicule Vocal Voix Voyelle

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