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Phone Segmentation for Japanese Triphthong Using Neural Networks.

, , , , , and . ITNG, page 470-475. IEEE Computer Society, (2011)

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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)GraATP: A Graph Theoretic Approach for Automated Theorem Proving in Plane Geometry., , and . CoRR, (2014)Distinctive Phonetic Feature (DPF) Extraction Based on MLNs and Inhibition/Enhancement Network., , and . IEICE Trans. Inf. Syst., 92-D (4): 671-680 (2009)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)Development of Analysis Rules for Bangla Part of Speech for Universal Networking Language., , , , , and . ITNG, page 797-802. IEEE Computer Society, (2011)A Hybrid POW-POS Implementation Against 51 percent Attack in Cryptocurrency System., , , , and . CloudCom, page 396-403. IEEE, (2019)Inhibition/Enhancement Network Based ASR using Multiple DPF Extractors., , , , and . J. Multim., 6 (5): 395-403 (2011)Effect of articulatory Δ and ΔΔ parameters on multilayer neural network based speech recognition., , , , , , , and . APCCAS, page 624-627. IEEE, (2010)Comparative Analysis of Intrusion Detection Systems and Machine Learning-Based Model Analysis Through Decision Tree., , and . IEEE Access, (2023)Bangla triphone HMM based word recognition., , , , , , , and . APCCAS, page 883-886. IEEE, (2010)