Phoneme classification experiments using radial basis functions
- 1 January 1989
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 461-467 vol.1
- https://doi.org/10.1109/ijcnn.1989.118620
Abstract
The application of a radial basis functions network to a static speech pattern classification problem is described. The radial basis functions network offers training times two to three orders of magnitude faster than backpropagation, when training networks of similar power and generality. Recognition results compare well with those obtained using backpropagation and a vector-quantized hidden Markov model on the same problem. A computationally efficient method of exactly solving linear networks in a noniterative fashion is also described. The method was applied to classification of vowels into 20 classes using three different types of input analysis and varying numbers of radial basis functions. The three types of input vectors consisted of linear-prediction-coding cepstral coefficient; formant tracks with frequency, amplitude, and bandwidth information; and bark-scaled formant tracks. All input analyses were supplemented with duration information. The best test results were obtained using the cepstral coefficients and 170 or more radial basis functions.Keywords
This publication has 7 references indexed in Scilit:
- Learning phoneme recognition using neural networksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Hierarchical phoneme discrimination by hidden Markov modelling using cepstrum and formant informationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Speedy alternatives to back propagationNeural Networks, 1988
- Self-Organization and Associative MemoryPublished by Springer Nature ,1988
- Globally optimising formant tracker using generalised centroidsElectronics Letters, 1987
- Speech quality evaluation using ‘‘phoneme-specific’’ sentencesThe Journal of the Acoustical Society of America, 1985
- Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern RecognitionIEEE Transactions on Electronic Computers, 1965