Subjective Evaluation of Spectral and Time Domain Cascading Algorithm for Speech Enhancement for Mobile Communication
In this paper, we present the comparative subjective analysis of Improved Minima Controlled Recursive Averaging (IMCRA) Algorithm, the Kalman filter and the cascading of IMCRA and Kalman filter algorithms. Performance of speech enhancement algorithms can be predicted in two different ways. One is the objective method of evaluation in which the speech quality parameters are predicted computationally. The second is a subjective listening test in which the processed speech signal is subjected to the listeners who judge the quality of speech on certain parameters. The comparative objective evaluation of these algorithms was analyzed in terms of Global SNR, Segmental SNR and Perceptual Evaluation of Speech Quality (PESQ) by the authors and it was reported that with cascaded algorithms there is a substantial increase in objective parameters. Since subjective evaluation is the real test to judge the quality of speech enhancement algorithms, the authenticity of superiority of cascaded algorithms over individual IMCRA and Kalman algorithms is tested through subjective analysis in this paper. The results of subjective listening tests have confirmed that the cascaded algorithms perform better under all types of noise conditions.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1314492Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 433
 S. Quackenbush, T. Berwell and M. Clements, Objective Measures of Speech Quality. Englewood Cliffs. NJ Prentice-Hall, 1988.
 Yi Hu, Philipos C. Loizou, “Subjective comparison and evaluation of speech enhancement algorithms,” NIH Speech Commun., 49 (7) pp. 588–601, July 2007.
 Philipos C. Loizou, Gibak Kim, “Reasons why current speech-enhancement algorithms do not improve speech intelligibility and suggested solutions,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 19, No.1, pp. 47-56, 2011.
 Harish Chander, Balwinder Singh, Ravinder Khanna, “Effective speech enhancement for mobile communication using cascading of frequency and time domain techniques,” SERSC, International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 10, No. 5, pp. 45-56, May 2017.
 Israel Cohen, “Noise spectrum Estimation in Adverse Environments: Improved Minima Controlled Recursive Averaging,” IEEE Transactions on Speech and Audio Processing, vol. 11, issue 5, pp. 466-475, Sep. 2003.
 Kybic, B. J., Kalman Filtering and Speech Enhancement, Polytechnic Thesis, Ecole Polytechnique De Lausanne, 1998.
 ITU-T P.835, Subjective test methodology for evaluating speech communication systems that include noise suppression algorithm, ITU-T Recommendation P.835, 2003.
 John S. Garofolo, Lori F. Lamel, William M. Fisher, Jonathan G. Fiscus, David S. Pallett and Nancy L. Dahlgren, Getting started With the DARPA TIMIT CD-ROM: An Acoustic Phonetic Continuous Speech Database, National Institute of Standards and Technology (NIST), Gaithersburg, Maryland, 1993.
 I. Cohen and B. Berdigo, “Spectral enhancement by tracking speech presence probability in subbands,” Proc. IEEE workshop on hands free speech Communication, HSC2001 Kypto, Japan, pp. 95-98, Apr. 2001.
 I. Cohen and B. Berdigo, “Speech enhancement for non-stationary noise environments,” Signal Processing, vol. 81, No. 11, pp. 2403-2418, Nov. 2001.