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By L.L. Ivanov

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By L.L. Ivanov

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Note that in the case of linear distortion, Eq. 3) reduces to the form of Eq. 2) when a cepstral representation is used. The two types of interference are sometimes lumped together into a simplified function Y = h(X) = HX + N. 4) Much of the work toward robust speech recognition in the past decade focused on estimation of the parameters, H and N, using Y [21]. Techniques such as spectral mean subtraction, signal bias removal, and maximum likelihood linear regression fall in this category. The robustness issue can also encompass normalization of the observation to compensate for the variation due to talker differences.

The confidence measure serves as a reference guide for a dialog system to provide an appropriate response to its users. To detect semantically significant parts and reject irrelevant portions in spontaneous utterances, a detectionbased approach has recently been investigated [23]. This combined recognition and verification strategy works well especially for ill-formed utterances. In order to build acoustic models more sophisticated than conventional HMMs, the dynamic Bayesian network has been investigated [58].

1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. , 26, 43–49. 47. , Lee, C. H. (2001). A structural Bayes approach to speaker adaptation. IEEE Trans. , 9, 276–287. 48. , Zweig, G. (2005). The IBM 2004 conversational telephone system for rich transcription. In: Proc. IEEE Int. Conf. on Acoustics, Speech, Signal Processing, Montreal, Canada, I, 205–208. 49. , Nakata, K. (1961). Recognition of Japanese vowels – preliminary to the recognition of speech. J.

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