Performance Evaluation of Loss Functions for Margin Based Robust Speech Recognition

Margin-based model estimation methods are applied for speech recognition to enhance the generalization capability of acoustic model by increasing the margin. An important aspects of margin based acoustic model for parameter estimation is that, the acoustic models are derived from soft margin concept and hinge loss function used in SVM as loss function to attained enhanced speech recognition performance. In this study, performance evaluation of loss functions (Logistic, Savage, Sigmoid) have been computed in the presence of white noise, pink noise, and brown noise with and without SVM classifiers to analyze the impact of noise on loss functions in comparison with hinge loss function used in SVM for parameter estimation in margin based acoustic model. Experimental results show that hinge loss function in the presence of pink noise and white noise have significant effects on isolated digits (0-9) in both pre-conditioned and recorded data samples in comparison with brown noise. Whereas hinge loss functions show serious anomalies with savage loss and sigmoid loss in term of performance and sigmoid loss function provides exceptionally good results in term of percentage error for all prescribed conditions.

@article{Ali2016,
  title     = {Performance Evaluation of Loss Functions for Margin Based Robust Speech Recognition},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {2},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Syed Abbas Ali and Maria Andleeb and Raheela Asif and Danish-ur-Rehman},
  doi       = {10.14569/IJACSA.2016.070249},
  url       = {https://doi.org/10.14569/IJACSA.2016.070249}
}