Syllabic Units Automatically Segmented Data for Continuous Speech Recognition

Authors

  • Madhav Singh Solanki SOEIT, Sanskriti University, Mathura, Uttar Pradesh, India Author

Keywords:

Speech Recognition, Hidden Markov Models, Databases, Natural Languages, Delay Effects

Abstract

We present novel approach for constant  speech processing in which the detection and recognition  tasks are separated A syllable is utilized as a measure  both to detection and localization. A minimal phase’s  group delay characteristic approach and an utterance  isolated style are used to segment the speech signal at the  boundaries of syllabic units. For two Indigenous  languages, an HMM recognizing system has been  created. Viterbi algorithm-based methods are suggested  to solve recognition problems caused by shifts in segment  borders and syllabic unit merging. 

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References

. Norris D, McQueen JM, Cutler A. Prediction, Bayesian inference and feedback in speech recognition. Lang Cogn Neurosci. 2016;

. Afouras T, Chung JS, Senior A, Vinyals O, Zisserman A. Deep Audio-visual Speech Recognition. IEEE Trans Pattern Anal Mach Intell. 2018;

. Zhang Z, Geiger J, Pohjalainen J, Mousa AED, Jin W, Schuller B. Deep learning for environmentally robust speech recognition: An overview of recent developments. ACM Transactions on Intelligent Systems and Technology. 2018.

. Xiong W, Droppo J, Huang X, Seide F, Seltzer ML, Stolcke A, et al. Toward Human Parity in Conversational Speech Recognition. IEEE/ACM Trans Audio Speech Lang Process. 2017;

. Herff C, Schultz T. Automatic speech recognition from neural signals: A focused review. Frontiers in Neuroscience. 2016.

. Swietojanski P, Ghoshal A, Renals S. Convolutional neural networks for distant speech recognition. IEEE Signal Process Lett. 2014;

. McKay CM, Rickard N, Henshall K. Intensity Discrimination and Speech Recognition of Cochlear Implant Users. JARO - J Assoc Res Otolaryngol. 2018;

. Deng L. Deep learning: From speech recognition to language and multimodal processing. APSIPA Transactions on Signal and Information Processing. 2016.

. Noda K, Yamaguchi Y, Nakadai K, Okuno HG, Ogata T. Audio-visual speech recognition using deep learning. Appl Intell. 2015;

. You CH, MA B. Spectral-domain speech enhancement for speech recognition. Speech Commun. 2017;

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Published

2021-11-30

Issue

Vol. 9 No. 6 (2021): International Journal of Innovative Research in Computer Science and Technology (Nov) 2021

Section

Articles

How to Cite

Syllabic Units Automatically Segmented Data for Continuous Speech Recognition . (2021). International Journal of Innovative Research in Computer Science & Technology, 9(6), 239–242. Retrieved from https://acspublisher.com/journals/index.php/ijircst/article/view/11191