Evaluation of Simultaneous Speech Detection Based on MFCC-DTW with Two-Stage Normalization

Alexandru - George Rusu, Radu-Sebastian Marinescu, Corneliu Burileanu, Dumitru Bica


In Air Traffic Control a serious safety risk is represented by undetected simultaneous transmissions from different airplanes. In this paper, we approach this issue through a speech analysis algorithm, which combines traditional Mel Frequency Cepstral Coefficients extraction, a new two-stage normalization and widely used Dynamic Time Warping. In this way, we were able to extend the simultaneous speech detection capability in Voice Communication Systems of Air Traffic Control. The results prove that this implementation is suitable for practical applications.

Full Text:



L. Friedrich, “Method and device for the detection of simultaneous dual emission of AM signals”, pattent DE102007037105 A1, 2008.

ED-137B - “Interoperability Standards for VoIP ATM Components”, European Organization for Civil Aviation Equipment, 2016.

R. S. Marinescu, C. Burileanu, “Voice activity detection for best signal selection in air traffic management and control systems”, in the Proc. 38th International Conference on Telecommunications and Signal Processing (TSP), Prague, Czech Republic, 2015.

R. S. Marinescu, “Best Signal Selection with Automatic Delay Compensation in VoIP Environment”, PhD Thesis, University Politehnica of Bucharest, Romania, 2013.

T. E. Tremain, “The government standard Linear Predictive Coding Algorithm: LPC10”, Speech Technology, Vol. 1, No.2, pp. 40-49, 1982.

P. Mermelstein, “Distance measures for speech recognition, psychological and instrumental,” in Pattern Recognition and Artificial Intelligence, C. H. Chen, Ed., pp. 374–388. Academic, US, 1976.

H. Hermansky, “Perceptual linear predictive (PLP) analysis of speech,” Journal of Acoustical Society of America, no. 87, pp. 1738-1752, 1990.

R. Bellman, “Dynamic Programming,” Princeton University Press, 1957.

M. Young, The Techincal Writers Handbook. Mill Valley, CA: University Science, 1989.

A. Ouzounov, “Robust Feature for Speech Detection”, Cybernetics and Information Technologies, vol.4, No.2, pp.3-14, Bulgaria, 2004.

L. S. Huang and C. H. Yang, “A novel approach to robust speech endpoint detection in car environments”, in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 1751-1754, Turkey, 2000.

T. Kristiansson, S. Deligne, P. Olsen, “Voicing Features for Robust Speech Detection”, in Proc. 6th Annual Conference of the International Speech Communication Association (ISCA)– INTERSPEECH, pp. 369-372, Portugal, 2005.

P. Boersma, “Accurate short-term analysis of the fundamental frequency and the harmonics-to-noise ratio of a sampled sound,” in IFA Proceedings. Institute of Phonetic Sciences, University of Amsterdam, pp. 97-110, 1993.

D. Talkin, “Speech Coding and Synthesis”, Elsevier Science B.V., 1995.

T. Drugman and A. Alwan, “Joint robust voicing detection and pitch estimation based on residual harmonics,” in Proc. Interspeech, Italy, 2011.

A. de Cheveign´e and H. Kawahara, “Yin, a fundamental frequency estimator for speech and music,” J. Acoust. Soc. Am., vol. 111, no. 4, pp. 1917-1930, 2002.

V. Ramasubramanian, A. Das, and V. Kumar, “Text-dependent speaker-recognition using one-pass dynamic program-ming”, in Proc. ICASSP’06, France, 2006.

N. Murali Krishna, P.V. Lakshmi, Y. Srinivas, J. Sirisha Devi, “Emotion Recognition using Dynamic Time Warping Technique for Isolated Words”, International Journal of Computer Science Issues, Vol. 8, Issue 5, No 1, pp. 306-309, 2011.

S. V. Chapaneri, “Spoken Digits Recognition using Weighted MFCC and Improved Features for Dynamic Time Warping”, International Journal of Computer Applications, vol, 40, no. 3, pp. 6-12, 2012.

W. Fu, X. Yang, and Y. Wang, “Heart sound diagnosis based on DTW and MFCC”, 3rd International Congress on Image and Signal Processing, pp. 2920-2923, 2010.

J. S. Garofolo, L. F. Lamel, W. M. Fisher, J. G. Fiscuc, D. S. Pallett, N. L. Dahlgren, V. Zue, “TIMIT Acoustic-Phonetic Continuous Speech Corpus LDC93S1”, Web Download. Philadelphia: Linguistic Data Consortium, 1993.

V. Andrei, H.Cucu, C. Burileanu, “Detecting overlapped speech on short timeframes using deep learning”, INTERSPEECH 2017, pp. 1198-1202, Sweden, 2017.

DOI: http://dx.doi.org/10.11601/ijates.v8i2.278


  • There are currently no refbacks.