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- W2786251182 abstract "The next step of isolated word recognition will be the sentence or continuous speech recognition. In this paper, an algorithm to estimates maximum posterior probabilities from input chhattisgarhi speech has proposed. To compute the accurate and precise maximum likelihoods, one need to extract the features from segmented words accurately. An implemented algorithm for automatic word segmentation gives up to 96.00% of average word boundary detection accuracy. Mel filter cepstral coefficients, delta cepstral and delta-delta cepstral speech characteristics have been extracted from the signals. Automatic segmented words from the speech have added and created a vocabulary. Further, 3-gram language model has implemented to resolve the ambiguity of similar pronunciation of the words. The last stage of the system is to compute maximum log likelihoods and maximum posterior probabilities of the word sequence. The experiments have been carried out on self-recorded 220 continuous chhattisgarhi speech, which consist of 2640 sentences and 330,000 words. We have used multilayer feedforward neural network (MLP) and Hidden markov model (HMM) for maximum likelihood estimation (MLE). The details of experimental analysis have presented in the paper." @default.
- W2786251182 created "2018-02-23" @default.
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- W2786251182 date "2017-04-01" @default.
- W2786251182 modified "2023-09-27" @default.
- W2786251182 title "Continuous speech recognition system for chhattisgarhi" @default.
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- W2786251182 doi "https://doi.org/10.1109/iccsp.2017.8286379" @default.
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