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- W2903098319 abstract "In this work, different Gaussian Mixture Model-Hidden Markov Model(GMM-HMM) based and Deep Neural Network (DNN-HMM) based models have been analyzed for speech recognition in Bangla language to build a voice search module for search engine pipilika 1. A small corpus of 9 hours of speech recordings from 49 different speakers was prepared for this work consisting of a vocabulary of 500 unique words. The lowest Word Error Rate(WER) for (GMM-HMM) based model was 3.96% and for (DNN-HMM) based model was 5.30%. To our best knowledge, this is the lowest WER for Bangla speech recognition for such vocabulary size." @default.
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- W2903098319 date "2018-09-01" @default.
- W2903098319 modified "2023-09-27" @default.
- W2903098319 title "Bangla Speech Recognition for Voice Search" @default.
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- W2903098319 doi "https://doi.org/10.1109/icbslp.2018.8554944" @default.
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