Advertisement

The Clustering Solution of Speech Recognition Models with SOM

  • Xiu-Ping Du
  • Pi-Lian He
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

This paper first introduces the system requirement and the system flow of the auto-plotting system. As the data points needed by the auto-plotting system coming from the remote speech signals, to reach high recognition accuracy, the Hidden Markov Model (HMM) approach was chosen as the speech recognition approach. Then the paper is detailed on the speaker dependent (SD), speaker independent (SI) and speaker adaptive (SA) speech recognition methods. We proposed the n-speech models SD system as the recognition system to gain the highest recognition performance in varying speech environments. However the system required that searching for the optimal model from the database should finish in 5 minutes, so the paper finally describes how the Self-Organizing Map (SOM) was used to pre clustering to the n-speech models, to decrease the time for speech recognition and results evaluation and decrease matching time, Experiments show the n-speech models SD system can select the best-matching model in the limited time and improve the average speech recognition accuracy to 97.2. It ideally suits the system requirements.

Keywords

Hide Markov Model Speech Recognition Cluster Solution Speech Model High Recognition Accuracy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Veeravalli, A.G., Pan, W.D., Adhami, R., Cox, P.G.: A Tutorial on Using Hidden Markov Models for Phoneme Recognition. In: SSST 2005 (eds.): Proceedings of the Thirty-Seventh Southeastern Symposium on System Theory, pp. 154–157. IEEE, Tuskegee (2005)CrossRefGoogle Scholar
  2. Legetter, C.J., Woodland, P.C.: Maximum Likelihood Linear Regression for Speaker Adaptation of Continuous Density Hidden Markov Models. Computer Speech and Language 9(2), 171–186 (1995)CrossRefGoogle Scholar
  3. Gauvain, J.L., Lee, C.H.: Maximum a Posteriori Estimation for Multivariate Gaussian Mixture Observations of Markov Chains. IEEE Trans. Speech and Audio Processing 2(2), 291–298 (1994)CrossRefGoogle Scholar
  4. Nishida, S., Ishii, K., Ura, T.: Adaptive Learning to Environment Using Self-Organizing Map and Its Application for Underwater Vehicles. In: UT 2004 (ed.) Proceedings of the 2004 International Symposium on Underwater Technology UT 2004, pp. 223–228. IEEE, Taipei (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xiu-Ping Du
    • 1
  • Pi-Lian He
    • 1
  1. 1.School of Electronic & Information EngineeringTianjin UniversityTianjinChina

Personalised recommendations