Abstract
In a forensic phone wiretapping investigation, a major problem is to get the full picture of the speakers involved. Typically, the wiretapped speech recordings are grouped using a clustering tool. The main disadvantage of such an approach is that in a bootstrapped scenario grouping errors accumulate. In this paper, we propose a visual approach to find similar speech recordings that probably stem from the same speaker. We first model the speech recordings and define suitable similarity measures between recordings. Then, through an approximate 2-D visualization of the inter-speech, similarities the investigator can identify clear groups of recordings and recordings that are harder to differentiate. We did extensive experiments on phone data of 50 speakers with 2 recordings per speaker. We tested quality of the 2-D visualization in relation to original high dimensional similarities. It turned out that for the original high dimensional similarity measure the nearest recording is almost always the one from the same speaker. In the 2-D visualization, we achieved that on average for all speech recordings a recording of the same speaker is among the 10 nearest recordings.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Schmidt-Nielsen, A., Crystal, T.H.: Human vs. machine speaker identification with telephone speech. In: Proceedings of the 5th International Conference on Spoken Language Processsing, Sydney, Australia (1998)
Ezzaidi, H., Rouat, J.: Speaker identification by computer and human evaluated on the SPIDRE corpus. Canadian Acoustics 28(3) (2000)
Alexander, A., Dessimoz, D., Botti, F., Drygajlo, A.: Aural and automatic forensic speaker recognition in mismatched conditions. Forensic Linguistics: The International Journal of Speech, Language and the Law 12(2) (2005)
Przybocki, M.A., Martin, A.F., Le, A.N.: NIST speaker recognition evaluation chronicles –part 2. In: Proceedings of the Odyssey 2006. Speaker and Language Recognition Workshop, San Juan, Puerto Rico, pp. 1–6 (2006)
Martin, A.F., Przybocki, M.A.: The NIST speaker recognition evaluations: 1996-2001. In: Proceedings of the Odyssey 2001. Speaker and Language Recognition Workshop, Crete, Greece (2001)
Gonzalez-Rodriguez, J., Drygajlo, A., Ramos-Castro, D., Garcia-Gomar, M., Ortega-Garcia, J.: Robust estimation, interpretation and assessment of likelihood ratios in forensic speaker recognition. Computer Speech and Language 20, 331–355 (2006)
Bimbot, F.B., Fredouille, J.F., Gravier, C., Magrin-Chagnolleau, G., Meignier, I., Merlin, S., Ortega-Garcia, T., Petrovska-Delacretaz, J., Reynolds, D.A.: A tutorial on text-independent speaker verification. EURASIP Journal on Applied Signal Processing, 430–451 (2004)
Kim, J.K., Shin, D.S., Bae, M.J.: A study on the improvement of speaker recognition system by voiced detection. In: Proceedings of the 2002 45th Midwest Symposium on Circuits and Systems, Tulsa, USA, vol. 3, pp. 324–327 (2002)
Krishnamachari, K., Yantorno, R.: Spectral autocorrelation ratio as a usability measure of speech segments under co-channel conditions. In: Proceedings of the IEEE International Symposium on Intelligent Signal Processing and Communication Systems, Honolulu, USA (2000)
Davis, S., Mermelstein, P.: Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Transactions on Acoustics Speech and Signal Processing 28(4) (1980)
Campbell, J.: 8: Speaker Recognition. In: Biometrics - Personal Identification in Networked Society. Springer, Heidelberg (2002)
Reynolds, D.A.: An overview of automatic speaker recognition technology. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Orlando, USA, pp. 4072–4075 (2002)
Atal, B.: Effectiveness of linear prediction characteristics of the speech wave for automatic speaker identification and verification. Journal of the Acoustical Society of America 55(6), 1304–1312 (1974)
Furui, S.: Speaker Recognition. In: Survey of the State of the Art in Human Language Technology, pp. 36–41. Cambridge University Press, Cambridge (1998)
Johnsen, M.H., Svendsen, T., Harborg, E.: Experiments on cepstral mean subtraction (CMS) and rasta-filtering applied to SAMPA phoneme recognition. In: COST 249, Nancy, France (1995)
Reynolds, D.A., Rose, R.: Robust text independent speaker identification using Gaussian mixture speaker models. IEEE Transactions on Speech and Audio Processing 3(1), 72–83 (1995)
Reynolds, D.A., Quatieri, T.F., Dunn, R.B.: Speaker verification using adapted Gaussian Mixture Models. Digital Signal Processing 10, 19–41 (2000)
Barras, C., Gauvain, J.L.: Feature and score normalization for speaker verification of cellular data. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Hong Kong, China, vol. 2, pp. 49–52 (2003)
Campbell, W.M., Sturim, D.E., Reynolds, D.A.: Support Vector Machines using GMM supervectors for speaker verification. IEEE Signal Processing Letters 13(5), 308–311 (2006)
Schwartz, G.: Estimation of the dimension of a model. Annals of Statistics 6, 461–464 (1978)
Torgerson, W.S.: Theory and methods of scaling. John Wiley, New York (1958)
Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Weiand, K.A., Bouten, J.S., Veenman, C.J. (2008). Similarity Visualization for the Grouping of Forensic Speech Recordings. In: Srihari, S.N., Franke, K. (eds) Computational Forensics. IWCF 2008. Lecture Notes in Computer Science, vol 5158. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85303-9_16
Download citation
DOI: https://doi.org/10.1007/978-3-540-85303-9_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85302-2
Online ISBN: 978-3-540-85303-9
eBook Packages: Computer ScienceComputer Science (R0)