A combined self-organizing feature map and multilayer perceptron for isolated word recognition

A neural network system which combines a self-organizing feature map and multilayer perception for the problem of isolated word speech recognition is presented. A new method combining self-organization learning and K-means clustering is used for the training of the feature map, and an efficient adaptive nearby-search coding method based on the 'locality' of the self-organization is designed. The coding method is shown to save about 50% computation without degradation in recognition rate compared to full-search coding. Various experiments for different choices of parameters in the system were conducted on the TI 20 word database with best recognition rates as high as 99.5% for both speaker-dependent and multispeaker-dependent tests. >

[1]  Teuvo Kohonen,et al.  The 'neural' phonetic typewriter , 1988, Computer.

[2]  John E. Shore,et al.  Discrete utterance speech recognition without time alignment , 1983, IEEE Trans. Inf. Theory.

[3]  Anthony Bladon,et al.  Acoustic phonetics, auditory phonetics, speaker sex and speech recognition: a thread , 1986 .

[4]  Lawrence R. Rabiner,et al.  A pattern recognition approach to voiced-unvoiced-silence classification with applications to speech recognition , 1976 .

[5]  L. R. Rabiner,et al.  Recognition of isolated digits using hidden Markov models with continuous mixture densities , 1985, AT&T Technical Journal.

[6]  Gary E. Kopec,et al.  Network-based isolated digit recognition using vector quantization , 1985, IEEE Trans. Acoust. Speech Signal Process..

[7]  Richard Lippmann,et al.  Neural Net and Traditional Classifiers , 1987, NIPS.

[8]  Geoffrey E. Hinton,et al.  Phoneme recognition using time-delay neural networks , 1989, IEEE Trans. Acoust. Speech Signal Process..

[9]  C. Un,et al.  Isolated word recognition based on finite-state vector quantization , 1986, ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[10]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[11]  G. R. Doddington,et al.  Computers: Speech recognition: Turning theory to practice: New ICs have brought the requisite computer power to speech technology; an evaluation of equipment shows where it stands today , 1981, IEEE Spectrum.

[12]  Bernhard R. Kämmerer,et al.  Experiments for isolated-word recognition with single- and two-layer perceptrons , 1990, Neural Networks.