This paper deals with the problem of estimating a transmitted string X, from the corresponding received string Y, which is a noisy version of X,. We assume that Y contains*any number of substitution, insertion, and deletion errors, and that no two consecutive symbols of X, were deleted in transmission. We have shown that for channels which cause independent errors, and whose error probabilities exceed those of noisy strings studied in the literature [ 121, at least 99.5 % of the erroneous strings will not contain two consecutive deletion errors. The best estimate X * of X, is defined as that element of H which minimizes the generalized Levenshtein distance D ( X/Y) between X and Y. Using dynamic programming principles, an algorithm is presented which yields X+ without computing individually the distances between every word of H and Y. Though this algorithm requires more memory, it can be shown that it is, in general, computationally less complex than all other existing algorithms which perform the same task. I.

Abstract not found

Support vector machines (SVMs) can be trained to detect manner transitions between phones and to identify the manner and place of articulation of any given phone. The SVMs can perform these tasks with high accuracy using a variety of acoustic representations. The SVMs generalize well to unseen test data if these data were created under identical conditions to the training corpus. Unseen acoustic data from different corpora present a problem for the SVM, even if these acoustic data were generated under similar conditions. The discriminant outputs of these SVMs are used to create both a hybrid SVM/HMM (hidden Markov model) phone recogni-tion system and a hybrid SVM/HMM word recognition system. There is a significant improvement in both phone and word recognition accuracy when these SVM discrim-inant features are used instead of mel frequency cepstral coefficients (MFCCs).

Support vector machines (SVMs) are trained to detect acoustic-phonetic landmarks, and to identify both the manner and place of articulation of the phones producing each landmark with high accuracy. The discriminant outputs of these SVMs are used as input features for a standard HMM based ASR system. There is a significant improvement in both the phone and word recognition accuracy when using these SVM discriminant features when compared to the phone and word recognition accuracy of an MFCC based recognizer.