The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
An efficient MFCC extraction method in speech recognition
Abstract
This paper introduces a new algorithm of extracting MFCC for speech recognition. The new algorithm reduces the computation power by 53% compared to the conventional algorithm. Simulation results indicate the new algorithm has a recognition accuracy of 92.93%. There is only a 1.5% reduction in recognition accuracy compared to the conventional MFCC extraction algorithm, which has an accuracy of 94.43%. However, the number of logic gates required to implement the new algorithm is about half of the MFCC algorithm, which makes the new algorithm very efficient for hardware implementation.
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Cites
Fundamentals of speech recognition
Rabiner, L. R., & Juang, B.-H. F. (1993) · Cited by 7,680
Davis, S., & Mermelstein, P. (1980) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1980.1163420 · Cited by 5,465
The scientist and engineer's guide to digital signal processing
Smith, S. W. (1997) · Cited by 2,799
Pearce, D., & Hirsch, H.-G. (2000) · 6th International Conference on Spoken Language Processing (ICSLP 2000) · doi:10.21437/icslp.2000-743 · Cited by 1,772
Signal modeling techniques in speech recognition
Picone, J. W. (1993) · Proceedings of the IEEE · doi:10.1109/5.237532 · Cited by 757
Comparison of different implementations of MFCC
Zheng, F., Zhang, G., & Song, Z. (2001) · Journal of Computer Science and Technology · doi:10.1007/bf02943243 · Cited by 591
On the effects of varying filter bank parameters on isolated word recognition
Dautrich, B. A., Rabiner, L. R., & Martin, T. (1983) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1983.1164172 · Cited by 102
On design and implementation of an embedded automatic speech recognition system
Phadke, S. V., Limaye, R., Verma, S., & Subramanian, K. (2004) · International Conference on VLSI Design · doi:10.1109/icvd.2004.1260914 · Cited by 31
Cited by
COVID-19 cough classification using machine learning and global smartphone recordings
Pahar, M., Klopper, M., Warren, R., & Niesler, T. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104572 · Cited by 290
Audio-visual emotion fusion (AVEF): A deep efficient weighted approach
Ma, Y., Hao, Y., Chen, M., Chen, J., Lu, P., & Košir, A. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.06.003 · Cited by 155
Clink, D. J., Crofoot, M. C., & Marshall, A. J. (2019) · Bioacoustics · doi:10.1080/09524622.2018.1426042 · Cited by 44
Chen, Z., Zhu, P., Qiu, W., Guo, J., & Li, Y. (2023) · International Journal of Language & Communication Disorders · doi:10.1111/1460-6984.12783 · Cited by 31
Classifying Speech of ASD Affected and Normal Children Using Acoustic Features
Mohanta, A., & Mittal, V. K. (2020) · 2020 National Conference on Communications (NCC) · doi:10.1109/ncc48643.2020.9056084 · Cited by 22
Metadata
- Authors
- Han, W., Chan, C.-F., Choy, C.-S., & Pun, K.-P.
- Venue
- 2006 IEEE International Symposium on Circuits and Systems
- Publisher
- IEEE
- Published
- 2006-09-22
- Pages
- 4
- ISSN
- 0271-4302 (ISSN-L)
- OpenAlex
- W1538323930
- Cited by
- 290
- References
- 8
- FWCI
- 2.16
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Han, W., Chan, C.-F., Choy, C.-S., & Pun, K.-P. (2006). An efficient MFCC extraction method in speech recognition. 2006 IEEE International Symposium on Circuits and Systems. https://doi.org/10.1109/iscas.2006.1692543
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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