Syllable-based large vocabulary continuous speech recognition
By Aravind Ganapathiraju, J. Hamaker, J. Picone, Mark Ordowski, G. Doddington. 2001
171 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Bo Xu
- Linhao Dong
- Shiyu Zhou
- Shuang Xu
- Parisa Haghani
- Zhongdi Qu
- Eugene Weinstein
- Pedro J. Moreno Mengibar
In Experimental and Cognitive Psychology
In Signal Processing
- Bo Xu
- Linhao Dong
- Shiyu Zhou
- Shuang Xu
- Parisa Haghani
- Zhongdi Qu
- Eugene Weinstein
- Pedro J. Moreno Mengibar
Citations in the corpus
- Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin Chinese (2018, methodology): “Ganapathiraju et al. have first shown that syllable based acoustic models can outperform context dependent phone based acoustic models with GMM [14].”
- Syllable-based acoustic modeling with CTC-SMBR-LSTM (2017, result): “[3] was among the first efforts to show that syllable-based acoustic models can beat CDphone-based acoustic models in terms of performance, using GMM.”