Title :
A Preliminary Study on Applying the Conditional Modeling to Automatic Dialect Classification
Author :
Huang, Rongqing ; Hansen, John H L
Author_Institution :
Center for Robust Speech Syst., Texas Univ., Richardson, TX
Abstract :
This paper addresses the advances in unsupervised dialect classification. There are no transcripts for both the training data and the testing data. In this study, we view the classification problem in speech in an recognition-based way instead of the conventional generative model-based approach and try to bypass the unknown transcript problem. The new algorithm is based on conditional model. The new algorithm has two notable advantages: first, it can train a statistical model without transcripts, so it can work in our transcript-free classification problem; second, the conditional model in the new algorithm can allow arbitrary feature representations, therefore, it can encode more discriminative features than the generative models such as hidden Markov model (HMM), which has to use the independent and local features due to the model restrictions. The conditional model used in the study is the conditional random fields (CRF). Further study on combining the generative model and conditional model is presented. In the Spanish dialect classification evaluation, the CRF and the combined modeling technique show some interesting results
Keywords :
natural languages; random processes; signal classification; signal representation; speech processing; speech recognition; statistical analysis; CRF; Spanish dialect classification evaluation; algorithm; automatic dialect classification; conditional modeling; conditional random fields; feature representation; speech recognition; statistical model; Adaptation model; Automatic speech recognition; Hidden Markov models; Histograms; Natural languages; Robustness; Speech recognition; Testing; Training data; Vocabulary;
Conference_Titel :
Signal Processing Symposium, 2006. NORSIG 2006. Proceedings of the 7th Nordic
Conference_Location :
Rejkjavik
Print_ISBN :
1-4244-0412-6
Electronic_ISBN :
1-4244-0413-4
DOI :
10.1109/NORSIG.2006.275207