For the acoustic models of embedded speech recognition systems, hidden Markov models (HMMs) are usually quantized and the original full space distributions are represented by combinations of a few quantized distribution prototypes. We propose a maximum likelihood objective function to train the quantized distribution prototypes. The experimental results show that the new training algorithm and the link structure adaptation scheme for the quantized HMMs reduce the word recognition error rate by 20.0%.
- Embedded speech recognition
- Maximum likelihood distribution clustering (MLDC)
- Quantized HMM
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Computer Science(all)
- Electronic, Optical and Magnetic Materials