Robust recognition of noisy speech over H.323 networks

In this paper, we investigate the performance of a speech recognizer on noisy speech transmitted over an H.323 channel, where the minimum mean-square error log spectra amplitude (MMSE-LSA) method is used to reduce the mismatch between training and deployment condition in order to achieve robust speech recognition. In the IP communication environment, one of the sources of distortion to the speech is packet loss. Of course, when ASR systems are used in adverse conditions, their performance degrades. In our work, we not only evaluate the impact of packet losses on speech recognition performance, but also explore the effects of uncorrelated additive noise on the performance. For measuring the influence of missing speech packets on the ASR system performance, we use a Soekris net 4501 IP simulator made by the Engineering Soekris Engineering Company, in order to control packet loss rate. To explore how additive acoustic noise affects the speech recognition performance, six types of noise sources are selected for use in our experiments. The experimental results indicate that the MMSE-LSA enhancement method apparently increased robustness for some type of additive noise under certain packet loss rates over the IP