Name
Title | ||
|---|---|---|
Sub-Phonetic Polynomial Segment Model For Large Vocabulary Continuous Speech Recognition |
Abstract | ||
|---|---|---|
Polynomial Segment Model (PSM) has opened up an alternative research direction for acoustic modeling. In our previous papers [ 1, 2] we proposed efficient incremental likelihood evaluation and EM training algorithms for PSM, that significantly improve the speed of PSM training and recognition. In this paper, we shift our focus to use PSM on large vocabulary recognition. Recognition via N-best re-scoring shows that PSM models out-performed HMM on the 5k closed vocabulary Wall Street Journal Nov 92 testset. Our best PSM model achieved 7.15% WER compare with 7.81% using 16 mixture HMM model. Specifically, we used sub-phonetic PSM that represents a phoneme as multiple independent segmental units that allows for more effective model sharing. Also, we derived and compared different top-down mixture growing approaches that are orders of magnitude more efficient than previously proposed bottom-up agglomerative clustering techniques. Experimental results show that the top-down clustering performs better than the bottom-up approaches. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/ICASSP.2005.1415083 | 2005 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1-5: SPEECH PROCESSING |
Keywords | Field | DocType |
k mean clustering,hmm,speech recognition,training data,acoustics,gold,hidden markov models,top down,bottom up,polynomials,acoustical engineering,testing | Hierarchical clustering,k-means clustering,Pattern recognition,Polynomial,Computer science,Speech recognition,Artificial intelligence,Cluster analysis,Hidden Markov model,Vocabulary,Model sharing,Acoustical engineering | Conference |
ISSN | Citations | PageRank |
1520-6149 | 7 | 0.57 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeff Siu-Kei Au-Yeung | 1 | 64 | 7.75 |
| Chak-Fai LI | 2 | 20 | 1.68 |
| Manhung Siu | 3 | 464 | 61.40 |