The high-dimensional feature vectors in our work represent the whole phoneme and they are formed by both averaging and concatenating shorttime feature vectors ...
LVQ-based speech recognition with high-dimensional context vectors. J. Mäntysalo, K. Torkkola, and T. Kohonen. Proceedings of International Conference on ...
Apr 10, 2012 · LVQ-based speech recognition with highdimensional context vectors. In Proceedings of the International Conference on Spoken Language Processing ...
In the framework of phonemic speech recognition using codebooks trained by Learning Vector Quantization (LVQ) together with Hidden Markov Models (HMMs), a novel ...
LVQ-based speech recognition with high- dimensional context vectors. In Proceedings of the International Conference on Spoken Language. Processing (ICSLP92) ...
A contextual VQ method based on the Markov Random Field (MRF) theory is proposed to model the speech feature vector space. Its superiority is confirmed by a ...
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Oct 16, 1992 · LVQ-based speech recognition with high-dimensional context vectors ... speech recognition based on context-dependent phoneme modeling. Y ...
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A recognition score of 73.5% for 23 phone classes was obtained using 224-dimensional phonetic feature vectors. 1:45. 55P4. Design of discriminant functions ...
Spoken Word Recognition Using MFCC and Learning Vector ...
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We propose a novel class of learning vector quantizers (LVQs) based on multivariate data ordering principles. A special case of the novel LVQ class is the ...
Several experiments have shown that if a set of feature vectors over a time window is concatenated into a higher-dimensional pattern vector, the recognition ...