Speech and Vision Lab

  • Increase font size
  • Default font size
  • Decrease font size
Home Publications
Signal-dependent matching for isolated word speech recognition systems
Research Area: Uncategorized Year: 1984
Type of Publication: Article Keywords: isolated word recognition; signal-dependent matching
Authors: B. Yegnanarayana, T. Sreekumar  
Journal: Signal Processing Volume: 7
Number: 2 Pages: 161 - 173
   
Note:
http://www.sciencedirect.com/science/article/B6V18-48V25S3-6S/2/086a1e75274045a98a8043c6d03341bf
Abstract:
A new approach to the design of IWSR systems is proposed in this paper. This involves a dynamic matching strategy based on the nature of the input speech segment. This is called signal-dependent matching. The computational complexity in the implementation of the proposed algorithm is significantly reduced by adopting a two stage approach in matching. In the first stage, the warping path between the test utterance and a reference utterance is determined. In the second stage, the distance between the utterances is computed along the path. There will be a slight degradation in the performance of a two stage approach as compared to the single stage approach, but this can be tolerated in view of the significant computational advantage. The performance degradation is more than compensated by the signal-dependent matching strategy in the second stage. To measure the improvement in the recognition performance, a new index of performance is defined, that reflects the characteristics of the distance matrix for a given vocabulary, rather than the characteristics of the confusion matrix. The performance of the signal-dependent matching algorithm is significantly better than the standard dynamic time warping matching algorithm for confusable as well as nonconfusable vocabulary. We also develop a signal-dependent matching algorithm, which takes into account some distortions in the input speech. As an example we offer the agorithm twice the same test utterance, once undistorted, once after a distortion. Our research until now indicates a improvement in automatic isolated word speech recognition systems while using signal-dependent parameter measuring and signal dependent matching.
Digital version