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Effect of articulatory Δ and ΔΔ parameters on multilayer neural network based speech recognition.

, , , , , , , and . APCCAS, page 624-627. IEEE, (2010)

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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)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)Distinctive Phonetic Feature (DPF) Extraction Based on MLNs and Inhibition/Enhancement Network., , and . IEICE Trans. Inf. Syst., 92-D (4): 671-680 (2009)Development of Analysis Rules for Bangla Part of Speech for Universal Networking Language., , , , , and . ITNG, page 797-802. IEEE Computer Society, (2011)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)Distinctive phonetic feature (DPF) based phone segmentation using hybrid neural networks., , , and . INTERSPEECH, page 94-97. ISCA, (2007)Smart reception: An artificial intelligence driven bangla language based receptionist system employing speech, speaker, and face recognition for automating reception services., , , , , , , , and . Eng. Appl. Artif. Intell., (2024)A Hybrid POW-POS Implementation Against 51 percent Attack in Cryptocurrency System., , , , and . CloudCom, page 396-403. IEEE, (2019)Effect of articulatory Δ and ΔΔ parameters on multilayer neural network based speech recognition., , , , , , , and . APCCAS, page 624-627. IEEE, (2010)