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Phone Segmentation for Japanese Triphthong Using Neural Networks.

, , , , , и . ITNG, стр. 470-475. IEEE Computer Society, (2011)

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Huda Kaufmann

Egbert Huda

Huda Kanaah

Christiane Huda

Armin Huda

 

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Distinctive Phonetic Feature (DPF) Extraction Based on MLNs and Inhibition/Enhancement Network., , и . IEICE Trans. Inf. Syst., 92-D (4): 671-680 (2009)Recurrent Neural Network Based Phoneme Recognition Incorporating Articulatory Dynamic Parameters., , , , , и . ACC (3), том 192 из Communications in Computer and Information Science, стр. 349-356. Springer, (2011)Local Feature or Mel Frequency Cepstral Coefficients - Which One Is Better for MLN-Based Bangla Speech Recognition?, , , , , и . ACC (2), том 191 из Communications in Computer and Information Science, стр. 154-161. Springer, (2011)Development of Analysis Rules for Bangla Part of Speech for Universal Networking Language., , , , , и . ITNG, стр. 797-802. IEEE Computer Society, (2011)Bangla triphone HMM based word recognition., , , , , , , и . APCCAS, стр. 883-886. IEEE, (2010)DPF-based japanese phoneme recognition using tandem MLNs., , , , , , , и . HIS, стр. 209-212. IEEE, (2010)Distinctive phonetic feature (DPF) based phone segmentation using hybrid neural networks., , , и . INTERSPEECH, стр. 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., , , , , , , , и . Eng. Appl. Artif. Intell., (2024)A Hybrid POW-POS Implementation Against 51 percent Attack in Cryptocurrency System., , , , и . CloudCom, стр. 396-403. IEEE, (2019)Effect of articulatory Δ and ΔΔ parameters on multilayer neural network based speech recognition., , , , , , , и . APCCAS, стр. 624-627. IEEE, (2010)