Abstract
Punjabi language has almost 105 million native speakers and faced the challenge of less resource. The Punjabi ASR system has little research as compared to other Indian languages. This paper examines the continuous vocabulary of Punjabi language using Sphinx toolkit. The proposed work has been implemented on speaker-independent and speaker-dependent speakers in different environmental conditions. The Punjabi ASR system has been trained on 442 phonetically rich sentences using 15 speakers (6 Male and 9 female). The system adopts MFCC at the front end and HMM at the modelling phase to extract and classify feature vectors. The simulation result demonstrates the performance improvement of 93.85% on speaker-dependent dataset and 89.96% on speaker-independent dataset.
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Bassan, N., Kadyan, V. (2019). An Experimental Study of Continuous Automatic Speech Recognition System Using MFCC with Reference to Punjabi Language. In: Sa, P., Bakshi, S., Hatzilygeroudis, I., Sahoo, M. (eds) Recent Findings in Intelligent Computing Techniques . Advances in Intelligent Systems and Computing, vol 707. Springer, Singapore. https://doi.org/10.1007/978-981-10-8639-7_28
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DOI: https://doi.org/10.1007/978-981-10-8639-7_28
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