DNN based continuous speech recognition system of Punjabi language on Kaldi toolkit
| Autor: | Jyoti Guglani, A. N. Mishra |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Linguistics and Language
Artificial neural network Computer science Speech recognition Word error rate Triphone Language and Linguistics Human-Computer Interaction ComputingMethodologies_PATTERNRECOGNITION Feature (machine learning) Deep neural networks Continuous speech recognition system Computer Vision and Pattern Recognition Mel-frequency cepstrum Software |
| Zdroj: | International Journal of Speech Technology. 24:41-45 |
| ISSN: | 1572-8110 1381-2416 |
| Popis: | This paper demonstrates the effect of incorporating Deep Neural Network techniques in speech recognition systems. Speech recognition through hybrid Deep Neural Networks on the Kaldi toolkit for the Punjabi language is implemented. Performance of the automatic speech recognition system drastically improves using DNN, and further Karel's DNN model gives better recognition performance as compared to Dan's DNN model. Out of MFCC and PLP features, the MFCC feature gives better results. The triphone model gives a lower word error rate than the monophone model, and 3-g gives a lower word error rate as compared to a 2-g model on the Kaldi toolkit for the continuous Punjabi speech recognition system. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink