Speech Recognition Based on Weight Function Neural Networks

Dai Yuan Zhang; Ran Zhao

doi:10.4028/www.scientific.net/AMM.513-517.1565

Paper Titles

A Comparison of NARX and BP Neural Network in Short-Term Building Cooling Load Prediction
p.1545

Fuzzy Based Distance Correction Algorithm for Digital Image Interpolation
p.1549

Real-Time Variance Calculation Method for Synchronous System Clock Based on Adaptive Exponential Smoothing
p.1555

Using Multi-Scale Gaussian Derivatives for Appearance-Based Recognition
p.1561

Speech Recognition Based on Weight Function Neural Networks
p.1565

Frequency Control of the Building Automation System in Rail Transit Stations Based on Multi-Objective Optimal Algorithm
p.1569

Accelerating Network Coding on Graphics Processors
p.1573

Study on Heterogeneous Cellular Network Frequency Resource Allocation Method Based on ASID
p.1577

An Accident Prediction and Diagnosis Method Based on Bayesian Network
p.1581

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 513-517Speech Recognition Based on Weight Function Neural...

Speech Recognition Based on Weight Function Neural Networks

Abstract:

Weight function neural network is a new kind of neural network developed in recent years, which has many advantages, such as finding globe minima directly, good performance of generalization, extracting some useful information inherent in the problems and so on. In this article, we apply orthogonal weight function neural network to the speech recognition. The result indicates that the weight function neural network has a good efficiency in speech recognition.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 513-517)

Pages:

1565-1568

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.513-517.1565

Citation:

Cite this paper

Online since:

February 2014

Authors:

Dai Yuan Zhang*, Ran Zhao

Keywords:

Neural Network (NN), Speech Recognition, Weight Function

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Daiyuan Zhang. New Theories and Methods on Neural Networks [M]. Beijing: Tsinghua University Press, (2006).

Google Scholar

[2] Rabiner L R, Juang B H. Fundamentals of speech recognition [M]. Upper Saddle River, NJ: Prentice hall, (1993).

Google Scholar

[3] Gandhiraj R, Sathidevi P S. Auditory-based wavelet packet filterbank for speech recgoniton using neural network [A] Institute of Electrical and Electronics Engineers Inc, 2007: 666-671.

DOI: 10.1109/adcom.2007.104

Google Scholar

[4] Rabiner, Lawrence R. A tutorial on hidden Markov models and selected applications in speech recognition[J]. Proceedings of IEEE, 77(2): 257-286.

DOI: 10.1109/5.18626

Google Scholar

[5] Myers, C, Rabiner, L, Rosenberg, A.E. Performance tradeoffs in dynamic time warping algorithms for isolated word recognition [J]. IEEE Transactions on neural networks, 1980, 28(6): 623-635.

DOI: 10.1109/tassp.1980.1163491

Google Scholar