Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization
Self-Organizing Maps
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
Speech Visualization based on Robust Self-organizing Map (RSOM) for the Hearing Impaired
BMEI '08 Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 02
Vector quantization for the efficient computation of continuous density likelihoods
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
The clustering solution of speech recognition models with SOM
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
The Self-Organizing Map (SOM) is widely applied for data clustering and visualization. In this paper, it is used to cluster Gaussians within the Hidden Markov Model (HMM) of the acoustic model for automatic speech recognition. The distance metric, neuron updating and map initialization of the SOM are adapted for the clustering of Gaussians. The neurons in the resulting map act as Gaussian clusters, which are used for Gaussian selection in the recognition phase to speed up the recognizer. Experimental results show that the recognition accuracy is kept while the decoding time can be reduced by 70%.