| Paper: | SP-P15.1 | ||
| Session: | Robustness in Noisy Environments | ||
| Time: | Friday, May 21, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Speech Processing: Robust Speech Recognition | ||
| Title: | ROBUST SPEECH RECOGNITION IN ADDITIVE AND CHANNEL NOISE ENVIRONMENTS USING GMM AND EM ALGORITHM | ||
| Authors: | Masakiyo Fujimoto; Ryukoku University | ||
| Yasuo Ariki; Kobe University | |||
| Abstract: | In this paper, we evaluated the speech recognition in real driving car environments by using a GMM based speech estimation method and an EM algorithm based channel noise estimation method. The GMM based speech estimation method proposed by Segura et al was not robust for channel noise such as an acoustic transfer function, a microphone characteristic and so on. To cope with this problem, we propose a channel noise estimation method based on the EM algorithm. Furthermore, we estimate the speech signal more accurately by using a speech GMM and a silence GMM instead of the GMM trained without speech/silence discrimination. Our proposed method has been evaluated on the AURORA3 tasks. In the evaluation results, the proposed method showed the significant improvement in the high-mismatched condition test of AURORA3 tasks. | ||
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