Abstract
In this paper we propose a quantized time series algorithm for spoken word recognition. In particular, we apply the algorithm to the task of spoken Arabic digit recognition. The quantized time series algorithm falls into the category of template matching approach, but with two important extensions. The first is that instead of selecting some typical templates from a set of training data, all the data is processed through vector quantization. The second extension consists of a built-in temporal structure within the quantized time series to facilitate the direct matching, instead of relying on time warping techniques. Experimental results have shown that the proposed approach outperforms the time warping pattern matching schemes in terms of accuracy and processing time.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Young, S.: Statistical modeling in Continuous Speech Recognition (CSR). In: International Conference on Uncertainty in Artificial Intelligence, pp. 562–571. Morgan Kaufmann, Seattle (2001)
Muda, L., Begam, M., Elamvazuthi, I.: Voice recognition algorithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) Techniques. J. of Computing 2(3), 138–143 (2010)
Zaharia, T., Segarceanu, S., Cotescu, M., Spataru, A.: Quantized Dynamic Time Warping (DTW) algorithm. In: 8th International Conference on Communications, Bucharest, pp. 91–94 (2010)
Rasanen, O.J., Laine, U.K., Altosaar, T.: Self-learning vector quantization for pattern discovery from speech. In: International Speech Communication Association, pp. 852–855. ISCA, Brighton (2009)
Frank, A., Asuncion, A.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2010), http://archive.ics.uci.edu/ml
Domeniconi, C., Gunopulos, D., Ma, S., Yan, B., Al-Razgan, M., Papadopoulos, D.: Locally adaptive metrics for clustering high dimensional data. J. Data Min. Knowl. Discov. 14(1), 63–97 (2007)
Karray, F., De Silva, C.: Soft computing and Intelligent systems design. Pearson Education Limited, Canada (2004)
Martin, M., Maycock, J., Schmidt, F.P., Kramer, O.: Recognition of Manual Actions Using Vector Quantization and Dynamic Time Warping. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds.) HAIS 2010, Part I. LNCS, vol. 6076, pp. 221–228. Springer, Heidelberg (2010)
Chanwoo, K., Seo, K.: Robust DTW-based recognition algorithm for hand-held consumer devices. J. of IEEE Transactions on Consumer Electronics 51(2), 699–709 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sun, J., Sun, J., Abida, K., Karray, F. (2012). A Novel Template Matching Approach to Speaker-Independent Arabic Spoken Digit Recognition. In: Kamel, M., Karray, F., Hagras, H. (eds) Autonomous and Intelligent Systems. AIS 2012. Lecture Notes in Computer Science(), vol 7326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31368-4_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-31368-4_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31367-7
Online ISBN: 978-3-642-31368-4
eBook Packages: Computer ScienceComputer Science (R0)