A self-learning neural tree network for recognition of speech features

Authors:
Mazin G. Rahim
Affiliations:
CAIP Center, Rutgers University, Piscataway, NJ
Venue:
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: plenary, special, audio, underwater acoustics, VLSI, neural networks - Volume I
Year:
1993

Citing 1
Cited 0

Neural trees-using neural nets in a tree classifier structure

ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference

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Abstract

This paper presents a Self-Learning Neural Tree Network (SL-NTN) for classification of speech features into phones. The SL-NTN employs a farthest-neighbor fuzzy-clustering algorithm to establish the intra-class correlation among speech phones, thus, splitting the phones in such a way to maximize the recognition performance while reducing the computational complexity. When evaluated on the 61 phones of the TIMIT database, the SL-NTN has shown to provide an 'optimal' trade-off between computational complexity and recognition performance. It also provides insight towards the interrelationship among the applied speech patterns.