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- W3163952456 abstract "Despite significant progress has been made in building of ASR system for various adult speech, whereas the children ASR system is still in infant stage for Indian languages. To build Punjabi children speech recognition is one such challenge because of unavailability of zero-speech corpus. In this paper, efforts have been made to build small vocabulary Punjabi continuous children speech corpus. In explored system, four variations of bMMI discriminative techniques have been perform on two context models: Dependent and Independent. Experiment result have shown that system attains Relative Improvement (RI) of 22-26% on fbMMI and fMMI acoustic model as compared to other approaches. Various combination of parameter has been implemented with variation in boosted parameter and iteration values to obtain optimal value of bMMI and fbMMI acoustic models." @default.
- W3163952456 created "2021-06-07" @default.
- W3163952456 creator A5005688115 @default.
- W3163952456 creator A5072742671 @default.
- W3163952456 date "2020-01-01" @default.
- W3163952456 modified "2023-09-26" @default.
- W3163952456 title "Feature Space Discriminatively Trained Punjabi Children Speech Recognition System Using Kaldi Toolkit" @default.
- W3163952456 doi "https://doi.org/10.2139/ssrn.3565906" @default.
- W3163952456 hasPublicationYear "2020" @default.
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