Title :
Formant estimation from cepstral coefficients using a feedforward memoryless neural network
Author :
Zahorian, Stephen A. ; Kelkar, Shubhangi ; Livingston, David
Author_Institution :
Dept. of Electr. & Comput. Eng., Old Dominion Univ., Norfolk, VA, USA
Abstract :
A method is described for estimating formants from cepstral coefficients using a memoryless feedforward neural network. The method was tested with vowel data from a large varied database. The neural network provided significantly better estimation of the formants than was possible with a linear transformation. However, the degradation in performance between training and test data suggests that the relationship between formants and cepstral coefficients is very complex and requires an extremely large amount of data for adequate neural network training
Keywords :
feedforward neural nets; learning (artificial intelligence); speech recognition; cepstral coefficients; feedforward memoryless neural network; formant estimation; large varied database; training; vowel data; Acoustical engineering; Automatic speech recognition; Cepstral analysis; Degradation; Design engineering; Encoding; Feedforward neural networks; Neural networks; Signal processing; Testing;
Conference_Titel :
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-0559-0
DOI :
10.1109/IJCNN.1992.227241