Abstract
Abstract This paper proposes a hybrid wavelet–neural network approach to classify speech for multifunctional control applications. The classification of the consonants (b,d,g) is the focus of this work. MultiResolution Wavelet Analysis (MRWA) was used to extract utterance features while a modular Artificial Neural Network (ANN) was used for classification. The performance of the proposed method was compared to that of the cepstrum method. The results show that MRWA are superior to the cepstrum in at least two points: higher recognition rate and consistent output demonstrating higher reliability.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
