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MLN-based Bangla ASR using context sensitive triphone HMM., , , and . I. J. Speech Technology, 14 (3): 183-191 (2011)Extraction of Local Features for Tri-Phone Based Bangla ASR., , , , and . ITNG, page 668-673. IEEE Computer Society, (2012)DPF-based japanese phoneme recognition using tandem MLNs., , , , , , , and . HIS, page 209-212. IEEE, (2010)Bangla triphone HMM based word recognition., , , , , , , and . APCCAS, page 883-886. IEEE, (2010)Hybridization of two stage Multilayer Neural Networks based Bangla ASR incorporating dynamic parameters., , , and . HIS, page 167-172. IEEE, (2011)Local Feature or Mel Frequency Cepstral Coefficients - Which One is Better for MLN-Based Bangla Speech Recognition?, , , , , and . CoRR, (2013)Phone Segmentation for Japanese Triphthong Using Neural Networks., , , , , and . ITNG, page 470-475. IEEE Computer Society, (2011)Local Feature or Mel Frequency Cepstral Coefficients - Which One Is Better for MLN-Based Bangla Speech Recognition?, , , , , and . ACC (2), volume 191 of Communications in Computer and Information Science, page 154-161. Springer, (2011)Recurrent Neural Network Based Phoneme Recognition Incorporating Articulatory Dynamic Parameters., , , , , and . ACC (3), volume 192 of Communications in Computer and Information Science, page 349-356. Springer, (2011)Inhibition/Enhancement Network Based ASR using Multiple DPF Extractors., , , , and . J. Multim., 6 (5): 395-403 (2011)