Voice Driven Applications in Non-stationary and Chaotic Environment
Automated operations based on voice commands will become more and more important in many applications, including robotics, maintenance operations, etc. However, voice command recognition rates drop quite a lot under non-stationary and chaotic noise environments. In this paper, we tried to significantly improve the speech recognition rates under non-stationary noise environments. First, 298 Navy acronyms have been selected for automatic speech recognition. Data sets were collected under 4 types of noisy environments: factory, buccaneer jet, babble noise in a canteen, and destroyer. Within each noisy environment, 4 levels (5 dB, 15 dB, 25 dB, and clean) of Signal-to-Noise Ratio (SNR) were introduced to corrupt the speech. Second, a new algorithm to estimate speech or no speech regions has been developed, implemented, and evaluated. Third, extensive simulations were carried out. It was found that the combination of the new algorithm, the proper selection of language model and a customized training of the speech recognizer based on clean speech yielded very high recognition rates, which are between 80% and 90% for the four different noisy conditions. Fourth, extensive comparative studies have also been carried out.
[1] B. R. Ramakrishnan, Recognition of Incomplete Spectrograms for
Robust Speech Recognition, Ph.D. dissertation, Dept. Electrical and
Computer Engineering, Carnegie Mellon University, 2000.
[2] M. L. Seltzer, B. Raj, and R. M. Stern, "Classifier-Based Mask
Estimation for Missing Feature Methods of Robust Speech
Recognition," Proc. of the International Conference of Spoken
Language Processing, Beijing, China, October, 2000.
[3] S.V., Milner, B.P, "Noise-adaptive hidden Markov models based on
Wiener filters", Proc. European Conf. Speech Technology, Berlin, Vol.
II, pp.1023-1026, 1993.
[4] "Acoustical and Environmental Robustness in Automatic Speech
Recognition". A. Acero. Ph. D.Dissertation, ECE Department, CMU,
Sept. 1990.
[5] Singh, R., Stern, R.M. and Raj, B., "Signal and Feature Compensation
Methods for Robust Speech Recognition," CRC Handbook on Noise
Reduction in Speech Applications, Gillian Davis, Ed. CRC Press, 2002.
[6] Singh, R., Raj, B. and Stern, R.M., "Model Compensation and Matched
Condition Methods for Robust Speech Recognition," CRC Handbook on
Noise Reduction in Speech Applications, Gillian Davis, Ed. CRC Press,
2002.
[7] Nadas, A., Nahamoo, D. and Picheny, M.A, "Speech recognition using
noise-adaptive prototypes", IEEE Trans. Acoust. Speech Signal Process.
Vol.37, No. 10, pp-1495- 1502, 1989.
[8] Mansour, D. and Juang, B.H, "The short-time modified coherence
representation and its application for noisy speech recognition", Proc.
IEEE Int.. Conf. Acoust. Speech Signal Process., New York, April
1988.
[9] S. Chakrabartty, Y. Deng and G. Cauwenberghs, "Robust Speech
Feature Extraction by Growth Transformation in Reproducing Kernel
Hilbert Space," Proc. IEEE Int. Conf. Acoustics Speech and Signal
Processing (ICASSP'2004), Montreal Canada, May 17-21, 2004.
[10] Ghitza, O., "Auditory nerve representation as a basis for speech
processing", in Advances in Speech Signal Processing, ed. by S. Furui
and M.M.Sondhi (Marcel Dekker, New York), Chapter 15, pp.453-485,
1992.
[11] H. Hermansky, "Perceptual linear predictive (PLP) analysis of speech",
J. Acoustic Soc. Am., vol. 87, no. 4, pp. 1738-1752, Apr. 1990.
[12] Y. Deng, S. Chakrabartty, and G. Cauwenberghs, "Analog Auditory
Perception Model for Robust Speech Recognition," Proc. IEEE Int. Joint
Conf. on Neural Network (IJCNN'2004), Budapest Hungary, July 2004.
[13] B. Raj et al., GRATZ Algorithm Summary, to be submitted.
[14] ''Sphinx-3 s3.3 Decoder", Mosur K. Ravishankar (aka Ravi Mosur),
Sphinx Speech Group, CMU.
[15] Slava M. Katz, "Estimation of Probabilities from Sparse Data for the
Language Model Component of a Speech Recognizer," in IEEE
Transactions on Acoustics, Speech and Signal Processing, Vol. 35(3),
pp. 400-401, March, 1987.
[16] Bhiksha Raj and Rita Singh, "Feature compensation with secondary
sensor measurements for robust speech recognition," Proc. EUSIPCO
2005, Antalya, Turkey, August 2005.
[17] Bhiksha Raj, Rita Singh and Paris Smaragdis, "Recognizing speech from
simultaneous speakers," Proc. INTERSPEECH 2005, Lisbon, Portugal,
September 2005.
[18] H . Hermansky, and N. Morgan, "RASTA processing of speech", IEEE
Trans. on Speech and Audio Proc., vol. 2, no. 4, pp. 578-589, Oct. 1994.
[19] http://www.btinternet.com/~a.c.walton/navy/smn-faq/smn2.htm
[20] http://spib.rice.edu/spib/data/signals/noise/
[1] B. R. Ramakrishnan, Recognition of Incomplete Spectrograms for
Robust Speech Recognition, Ph.D. dissertation, Dept. Electrical and
Computer Engineering, Carnegie Mellon University, 2000.
[2] M. L. Seltzer, B. Raj, and R. M. Stern, "Classifier-Based Mask
Estimation for Missing Feature Methods of Robust Speech
Recognition," Proc. of the International Conference of Spoken
Language Processing, Beijing, China, October, 2000.
[3] S.V., Milner, B.P, "Noise-adaptive hidden Markov models based on
Wiener filters", Proc. European Conf. Speech Technology, Berlin, Vol.
II, pp.1023-1026, 1993.
[4] "Acoustical and Environmental Robustness in Automatic Speech
Recognition". A. Acero. Ph. D.Dissertation, ECE Department, CMU,
Sept. 1990.
[5] Singh, R., Stern, R.M. and Raj, B., "Signal and Feature Compensation
Methods for Robust Speech Recognition," CRC Handbook on Noise
Reduction in Speech Applications, Gillian Davis, Ed. CRC Press, 2002.
[6] Singh, R., Raj, B. and Stern, R.M., "Model Compensation and Matched
Condition Methods for Robust Speech Recognition," CRC Handbook on
Noise Reduction in Speech Applications, Gillian Davis, Ed. CRC Press,
2002.
[7] Nadas, A., Nahamoo, D. and Picheny, M.A, "Speech recognition using
noise-adaptive prototypes", IEEE Trans. Acoust. Speech Signal Process.
Vol.37, No. 10, pp-1495- 1502, 1989.
[8] Mansour, D. and Juang, B.H, "The short-time modified coherence
representation and its application for noisy speech recognition", Proc.
IEEE Int.. Conf. Acoust. Speech Signal Process., New York, April
1988.
[9] S. Chakrabartty, Y. Deng and G. Cauwenberghs, "Robust Speech
Feature Extraction by Growth Transformation in Reproducing Kernel
Hilbert Space," Proc. IEEE Int. Conf. Acoustics Speech and Signal
Processing (ICASSP'2004), Montreal Canada, May 17-21, 2004.
[10] Ghitza, O., "Auditory nerve representation as a basis for speech
processing", in Advances in Speech Signal Processing, ed. by S. Furui
and M.M.Sondhi (Marcel Dekker, New York), Chapter 15, pp.453-485,
1992.
[11] H. Hermansky, "Perceptual linear predictive (PLP) analysis of speech",
J. Acoustic Soc. Am., vol. 87, no. 4, pp. 1738-1752, Apr. 1990.
[12] Y. Deng, S. Chakrabartty, and G. Cauwenberghs, "Analog Auditory
Perception Model for Robust Speech Recognition," Proc. IEEE Int. Joint
Conf. on Neural Network (IJCNN'2004), Budapest Hungary, July 2004.
[13] B. Raj et al., GRATZ Algorithm Summary, to be submitted.
[14] ''Sphinx-3 s3.3 Decoder", Mosur K. Ravishankar (aka Ravi Mosur),
Sphinx Speech Group, CMU.
[15] Slava M. Katz, "Estimation of Probabilities from Sparse Data for the
Language Model Component of a Speech Recognizer," in IEEE
Transactions on Acoustics, Speech and Signal Processing, Vol. 35(3),
pp. 400-401, March, 1987.
[16] Bhiksha Raj and Rita Singh, "Feature compensation with secondary
sensor measurements for robust speech recognition," Proc. EUSIPCO
2005, Antalya, Turkey, August 2005.
[17] Bhiksha Raj, Rita Singh and Paris Smaragdis, "Recognizing speech from
simultaneous speakers," Proc. INTERSPEECH 2005, Lisbon, Portugal,
September 2005.
[18] H . Hermansky, and N. Morgan, "RASTA processing of speech", IEEE
Trans. on Speech and Audio Proc., vol. 2, no. 4, pp. 578-589, Oct. 1994.
[19] http://www.btinternet.com/~a.c.walton/navy/smn-faq/smn2.htm
[20] http://spib.rice.edu/spib/data/signals/noise/
@article{"International Journal of Electrical, Electronic and Communication Sciences:58247", author = "C. Kwan and X. Li and D. Lao and Y. Deng and Z. Ren and B. Raj and R. Singh and R. Stern", title = "Voice Driven Applications in Non-stationary and Chaotic Environment", abstract = "Automated operations based on voice commands will become more and more important in many applications, including robotics, maintenance operations, etc. However, voice command recognition rates drop quite a lot under non-stationary and chaotic noise environments. In this paper, we tried to significantly improve the speech recognition rates under non-stationary noise environments. First, 298 Navy acronyms have been selected for automatic speech recognition. Data sets were collected under 4 types of noisy environments: factory, buccaneer jet, babble noise in a canteen, and destroyer. Within each noisy environment, 4 levels (5 dB, 15 dB, 25 dB, and clean) of Signal-to-Noise Ratio (SNR) were introduced to corrupt the speech. Second, a new algorithm to estimate speech or no speech regions has been developed, implemented, and evaluated. Third, extensive simulations were carried out. It was found that the combination of the new algorithm, the proper selection of language model and a customized training of the speech recognizer based on clean speech yielded very high recognition rates, which are between 80% and 90% for the four different noisy conditions. Fourth, extensive comparative studies have also been carried out.
", keywords = "Non-stationary, speech recognition, voice commands.", volume = "1", number = "7", pages = "1023-9", }
