Abstract
A baseline ASR system does not perform better due to improper modeling of training data. Training of system through conventional HMM technique faced the issue of on or near manifold in data space. In this paper, Hybrid SGMM-HMM approach is compared with baseline GMM-HMM technique on Punjabi continuous simple sentences speech corpus. It examined the hybridized HMM technique: SGMM-HMM to overcome the problem of sharing of state parameter information throughout the training and testing of system. The system testing is performed on Kaldi 4.3.11 toolkit using MFCC and GFCC approaches at the front end of the system. The result obtained on SGMM-HMM modeling technique generates an improvement of 3–4% over GMM-HMM approach. The experiments are performed on real environment dataset.
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Kadyan, V., Kaur, M. (2020). SGMM-Based Modeling Classifier for Punjabi Automatic Speech Recognition System. In: Elçi, A., Sa, P., Modi, C., Olague, G., Sahoo, M., Bakshi, S. (eds) Smart Computing Paradigms: New Progresses and Challenges. Advances in Intelligent Systems and Computing, vol 767. Springer, Singapore. https://doi.org/10.1007/978-981-13-9680-9_12
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DOI: https://doi.org/10.1007/978-981-13-9680-9_12
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