Abstract
For a hidden Markov model (HMM) based speech recognition system it is desirable to combine enhancement of the acoustical signal and statistical representation of model parameters, ensuring both a high quality speech signal and an appropriately trained HMM. In this paper the incremental variant of maximum a posteriori (MAP) estimation is used to adjust the parameters of a talker-independent HMM-based speech recognition system to accurately recognize speech data acquired with a microphone-array. The approach is novel for a microphone-array speech recognition task in that a robust talker-independent model is derived from a baseline system using a relatively small amount of data for training. The results show that (1) MAP training significantly improves recognition performance compared to the baseline, and (2) beamforming signal enhancement out-performs single-channel enhancement before and after the adaptive MAP training.
| Original language | English |
|---|---|
| Pages (from-to) | 897-900 |
| Number of pages | 4 |
| Journal | Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing |
| Volume | 2 |
| Publication status | Published - 1996 |
| Externally published | Yes |
| Event | Proceedings of the 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 6) - Atlanta, GA, USA Duration: 7 May 1996 → 10 May 1996 |
ASJC Scopus subject areas
- Software
- Signal Processing
- Electrical and Electronic Engineering