On compensating the Mel-frequency cepstral coefficients for noisy speech recognition

ABSTRACT

This paper describes a novel noise-robust automatic speech recognition (ASR) front-end that employs a combination of Mel-filterbank output compensation and cumulative distribution mapping of cepstral coefficients with truncated Gaussian distribution. Recognition experiments on the Aurora II connected digits database reveal that the proposed front-end achieves an average digit recognition accuracy of 84.92% for a model set trained from clean speech data. Compared with the ETSI standard Mel-cepstral front-end, the proposed front-end is found to obtain a relative error rate reduction of around 61%. Moreover, the proposed front-end can provide comparable recognition accuracy with the ETSI advanced front-end, at less than half the computation load.

REFERENCES

• 1: Choi, E. (2004): Noise Robust Front-end for ASR using Spectral Subtraction, Spectral Flooring and Cumulative Distribution Mapping. Proc. 10th Australian Int. Conf. on Speech Science and Technology, pp. 451-456.
• 2: Li Deng , Xuedong Huang, Challenges in adopting speech recognition, Communications of the ACM, v.47 n.1, January 2004 [doi>10.1145/962081.962108]
• 3: Dharanipragada, S. and Padmanabhan, M. (2000): A Nonlinear Unsupervised Adaptation Technique for Speech Recognition. Proc. Int. Conf. on Spoken Language Processing, Vol. 4, pp. 556-559.
• 4: Ephraim, Y. (1992): A Bayesian Estimation Approach for Speech Enhancement Using Hidden Markov Models. IEEE Trans. Signal Processing, Vol. 40, No. 4, pp. 725-735.
• 5: ETSI (2000): Speech Processing, Transmission and Quality Aspects (STQ); Distributed Speech Recognition; Front-end Feature Extraction Algorithm; Compression Algorithms. ETSI standard document ES 201 108.
• 6: ETSI (2002): Speech Processing, Transmission and Quality Aspects (STQ); Distributed Speech Recognition; Advanced Front-end Feature Extraction Algorithm; Compression Algorithm. ETSI standard document ES 202 050.
• 7: Hermansky, H. (1990): Perceptual Linear Predictive (PLP) Analysis of Speech. Journal Acoustical Society of America (JASA), Vol. 87 (4), pp. 1738-1752.
• 8: Hirsch, H.G. and Pearce, D. (2000): The AURORA Experimental Framework for the Performance Evaluation of Speech Recognition Systems Under Noise Conditions. Proc. ISCA ITRW ASR2000, pp. 181-188.
• 9: Huang, C., Wang, H. and Lee, C. (2001): An SNR-Incremental Stochastic Matching Algorithm for Noisy Speech Recognition. IEEE Trans. Speech and Audio Processing, Vol. 9, No. 8, pp. 866-873.
• 10: Lawrence Rabiner , Biing-Hwang Juang, Fundamentals of speech recognition, Prentice-Hall, Inc., Upper Saddle River, NJ, 1993
• 11: John C. Russ, The image processing handbook (2nd ed.), CRC Press, Inc., Boca Raton, FL, 1995
• 12: Sankar, A. and Lee, C.H. (1996): A Maximum Likelihood Approach to Stochastic Matching for Robust Speech Recognition. IEEE Trans. Speech and Audio Processing, Vol. 4, pp. 190-202.
• 13: Stevens, S.S. (1957): On the Psychological Law. Psychological Review, Vol. 64, pp. 153-181.
• 14: Vaseghi, S.V. (2000): Advanced Digital Signal Processing and Noise Reduction. Wiley Press.
• 15: Yao, K., Paliwal, K.K. and Nakamura, S. (2001): Sequential Noise Compensation by a Sequential Kullback Proximal Algorithm. Proc. European Conf. on Speech Communication and Technology, pp. 1139-1142.
• 16: Zhang, Z. and Furui, S. (2004): Piecewise-linear Transformation-based HMM Adaptation for Noisy Speech. Speech Communication, Vol. 42, Issue 1, pp. 43-58.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
          Subjects: Speech recognition and synthesis

Additional Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.5 Sound and Music Computing
          Subjects: Signal analysis, synthesis, and processing; Modeling

General Terms:
Algorithms, Design, Theory

Keywords:
frontend processing, mel-frequency cepstral coefficient, noise robustness, speech recognition