N-Gram Language Model based Continuous Voiced Odia Digit Recognition
Prithviraj Mohanty1, Ajit Kumar Nayak2
1Prithviraj Mohanty, Department of CS&IT, ITER, S’O’A (Deemed to be) University, Bhubaneswar, India.
2Ajit Kumar Nayak, Department of CS&IT, ITER, S’O’A (Deemed to be) University, Bhubaneswar, India.
Manuscript received on 13 March 2019 | Revised Manuscript received on 19 March 2019 | Manuscript published on 30 July 2019 | PP: 4565-4574 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3273078219/19©BEIESP | DOI: 10.35940/ijrte.B3273.078219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the enormous improvement in the area of signal processing, speech processing systems are creating a massive impact in recognizing the voices, controlling the commands and making as communication interfaces. A continuous speech recognition system is essential for voice identification hands free system used as a voice dialer, voice originated security systems and voice based automatic electronic machines. The proposed work suggests a finest speaker independent continuous voiced digit recognition for Odia language. The model integrates the concept of Mel Frequency Cepstral Coefficient (MFCC) and continuous density Hidden Markov Model (HMM), relating to speech parameterization and recognition respectively. The performance of the model is explored for different levels of HMM like word-level and phoneme-level. Further the model output is evaluated using different N-Gram approaches of the language model. Finally it is shown that the model using phoneme-level HMM with a tri-gram language model is superior to other methodologies.
Index Terms: MFCC, HMM, Phoneme-Level, N-Gram, Language Model
Scope of the Article: Natural Language Processing