- B. Raj, E. Gouvêa, R. Stern. None. Ble Mixture Gaussian Distributions with the following Means and Variances: 4.2. Performance of Ratz on Speech Recogni- Tion in Noise 5. Summary Acknowledgements -0.2 0.2 0.4 0.6 0.8 1.0 1.2. Abstract: The data were contaminated by artificial noise which had a two-dimensional Gaussian distribution with a mean value of 0.50 and a variance of 0.001 for both dimensions. The covariance matrix was diagonal. Figure 3 compares the effectiveness of both algorithms in estimating clean speech vectors in the presence of this noise. The filled circles in the two panels of Figure 3 indicate the locations of the clean speech vectors, and the filled squares indicate the locations of the noisy speech before compensation was applied. The dashed arrows and continuous arrows indicate the corrections provided by the stereo RATZ and blind RATZ algorithms, respectively. It can be seen that blind RATZ provides a compensation that is almost as complete as that provided by stereo RATZ. Similar results have been observed on real speech statistics. The effectiveness of the RATZ algorithms was evaluated using the CMU census database [1], a continuous speaker-independent database consisting of strings of letters, numbers, and a few control words with a total vocabulary size of 107 words. The training set consisted of 1018 sentences stereo recorded over a noise-canceling close-talking microphone (CLSTK) and the desktop Crown-PZM6FS microphone (CRPZM). The testing set consisted of 140 stereo-recorded sentences. The adaptation set used by all compensation algorithms had a size of 400 stereo sentences randomly chosen from the training set. The SPHINX-II continuous speech recognition system was used. In Table 1 we compare the recognition error rate of several versions of RATZ to the error rates of previous algorithms developed at CMU [1,2]. The system was trained on clean speech and the adaptation set was used to learn the noisy speech distributions. Compensation algorithms were applied to the noisy data before recognition. A recognition error rate of 12.4% was achieved using the CLSTK microphone with no compensation. It can be seen that the RATZ algorithms perform better than virtually all of the previous CMU algorithms. We also note that accounting for the change in the variance improves performance. Finally, blind RATZ, which does not make explicit use of the stereo training data performs almost as well as stereo RATZ, and performs better that some of the previous algorithms that use stereo training, such as SDCN. In this paper we introduce a new family of algorithms to deal with the problem of speech recognition in noisy environments. We analyze the effects of noise on the statistics of common speech features , …
