Name
Abstract | ||
|---|---|---|
In tandem systems, the outputs of multi-layer perceptron (MLP) classifiers have been successfully used as features for HMM-based automatic speech recognition. In this paper, we propose a data-driven clustered hierarchical tandem system that yields improved performance on a large-vocabulary broadcast news transcription task. The complicated global learning for a large monolithic MLP classifier is divided into simpler tasks, in which hierarchical structures clustered based on the outputs of a monolithic MLP are used to alleviate phone confusion. The proposed approach yields error rate reductions of up to 16.4% over MFCC features alone. |
Year | Venue | Field |
|---|---|---|
2008 | INTERSPEECH 2008: 9TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2008, VOLS 1-5 | Tandem,Data-driven,Pattern recognition,Computer science,Speech recognition,Artificial intelligence |
DocType | Citations | PageRank |
Conference | 3 | 0.43 |
References | Authors | |
4 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuo-Yiin Chang | 1 | 13 | 1.77 |
| Lin-shan Lee | 2 | 1525 | 182.03 |