Abstract
This work processes linear prediction (LP) residual in the time domain at three different levels, extracts speaker information, and demonstrates their significance and also different nature for text-independent speaker recognition. The subsegmental analysis considers LP residual in blocks of 5 msec with shift of 2.5 msec to extract speaker information. The segmental analysis extracts speaker information by processing in blocks of 20 msec with shift of 2.5 msec. The suprasegmental speaker information is extracted by viewing in blocks of 250 msec with shift of 6.25 msec. The speaker identification and verification studies performed using NIST-99 and NIST-03 databases demonstrate that the segmental analysis provides best performance followed by subsegmental analysis. The suprasegmental analysis gives the least performance. However, the evidences from all the three levels of processing seem to be different and combine well to provide improved performance, demonstrating different speaker information captured at each level of processing. Finally, the combined evidence from all the three levels of processing together with vocal tract information further improves the speaker recognition performance.
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References
Ananthapadmanabha, T. V., & Yegnanarayana, B. (1979). Epoch extraction from linear prediction residual for identification of closed glottis interval. IEEE Transactions on Acoustics, Speech, and Signal Processing, ASSP-27, 309–319.
Atal, B. S. (1972). Automatic speaker recognition based on pitch contours. The Journal of the Acoustical Society of America, 52(6), 1687–1697.
Atal, B. S. (1974). Effectiveness of linear prediction characteristics of the speech wave for automatic speaker identification and verification. The Journal of the Acoustical Society of America, 55(6), 1304–1312.
Cohen, L. (1995). Time-frequency analysis: theory and application. Signal processing series. Englewood Cliffs: Prentice Hall.
Davis, S. B., & Mermelstein, P. (1980). Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Transactions on Acoustics, Speech, and Signal Processing, 28(28), 357–366.
Ezzaidi, H., & Rouat, J. (2004). Pitch and MFCC dependent GMM models for speaker identification systems. In IEEE int. conf. on electrical and computer eng.: Vol. 1.
Farrus, M., & Hernando, J. (2009). Using jitter and shimmer in speaker verification. Signal Processing, 3(4), 247–257.
Furui, S. (1981). Cepstral analysis technique for automatic speaker verification. IEEE Transactions on Acoustics, Speech, and Signal Processing, 29(2), 254–272.
Hayakawa, S., Takeda, K., & Itakura, F. (1997). Speaker identification using harmonic structure of lp-residual spectrum. In Lecture notes in computer science: Vol. 1206. Audio- and video-based biometric personal authentification (pp. 253–260). Berlin: Springer.
Huang, W., Chao, J., & Zhang, Y. (2008). Combination of pitch and MFCC GMM supervectors for speaker verification. In IEEE int. conf. on audio, language and image process (ICALIP) (pp. 1335–1339).
Jankowski, C., Kalyanswamy, A., Basson, S., & Spitz, J. (1990). NTIMIT: A phonetically balanced, continuous speech, telephone bandwidth speech database. In Int. conf. on acoust. speech and signal process. (ICASSP), Albuquerque, NM (pp. 109–112).
Makhoul, J. (1975). Linear prediction: A tutorial review. Proceedings of the IEEE, 63(4), 561–580.
Martin, A., Doddington, G., Kamm, T., Ordowski, M., & Przybocki, M. (1997). The DET curve in assessment of detection task performance. In Proc. Eur. conf. on speech communication technology, Rhodes, Greece, Vol. 4 (pp. 1895–1898).
Mary, L., & Yegnanarayana, B. (2008). Extraction and representation of prosodic features for language and speaker recognition. Speech Communication, 50, 782–796.
Mashao, D. J., & Skosan, M. (2006). Combining classifier decisions for robust speaker identification. Pattern Recognition, 39, 147–155.
Murthy, K. S. R., & Yegnanarayana, B. (2008). Epoch extraction from speech signal. IEEE Transactions on Speech and Audio Processing, 16(8), 1602–1613.
Murthy, K. S. R., & Yegnanarayana, B. (2009). Characterization of glottal activity from speech signal. IEEE Signal Processing Letters, 16(6), 469–472.
Murty, K. S. R., & Yegnanarayana, B. (2006). Combining evidence from residual phase and MFCC features for speaker recognition. IEEE Signal Processing Letters, 13(1), 52–55.
Murty, K. S. R., Prasanna, S. R. M., & Yegnanarayana, B. (2004). Speaker specific information from residual phase. In Int. conf. on signal proces. and comm. (SPCOM).
Nist speaker recognition evaluation plan (2003). In: Proc. NIST speaker recognition workshop, College Park, MD.
Pati, D., & Prasanna, S. R. M. (2010). Speaker information from subband energies of linear prediction residual. In Proc. NCC (pp. 1–4).
Plumpe, M. D., Quatieri, T. F., & Reynolds, D. A. (1999). Modelling of glottal flow derivative waveform with application to speaker identification. IEEE Transactions on Speech and Audio Processing, 7(5), 569–586.
Prasanna, S. R. M., Gupta, C. S., & Yegnanarayana, B. (2006). Extraction of speaker-specific excitation information from linear prediction residual of speech. Speech Communication, 48, 1243–1261.
Przybocky, M., & Martin, A. (2000). The NIST-1999 speaker recognition evaluation- an overview. Digital Signal Processing, 10, 1–18.
Reynolds, D. A. (1994). Experimental evaluation of features for robust speaker identification. IEEE Transactions on Speech and Audio Processing, 2(4), 639–643.
Reynolds, D. A. (1995). Speaker identification and verification using gaussian mixture speaker models. Speech Communication, 17, 91–108.
Reynolds, D. A., & Rose, R. C. (1995). Robust text-independent speaker identification using gaussian mixture speaker models. IEEE Transactions on Speech and Audio Processing, 3(1), 4–17.
Sonmez, K., Shriberg, E., Heck, L., & Weintraub, M. (1998). Modeling dynamic prosodic variation for speaker verification. In Proc. ICSLP’ 98: Vol. 7 (pp. 3189–3192).
Thevenaz, P., & Hugli, H. (1995). Usefulness of the LPC-residue in text-independent speaker verification. Speech Communication, 17, 145–157.
Wolf, J. J. (1972). Efficient acoustic parameters for speaker recognition. The Journal of the Acoustical Society of America, 51(2), 2044–2055.
Yegnanarayana, B., & Prasanna, S. R. M. (2010). Analysis of instantaneous F0 contours from two speakers mixed signal using zero frequency filtering. In Int. conf. on acoust. speech and signal process. (ICASSP), Dallas, Texas, USA (pp. 5074–5077).
Yegnanarayana, B., Reddy, K. S., & Kishore, S. P. (2001). Source and system feature for speaker recognition using AANN models. In Proc. IEEE int. con. acoust. speech and signal processing, Salt Lake City, UT, USA, May 2001 (pp. 409–412).
Yegnanarayana, B., Prasanna, S. R. M., Zachariah, J. M., & Gupta, C. S. (2005). Combining evidences from source, suprasegmental and spectral features for fixed-text speaker verification study. IEEE Transactions on Speech and Audio Processing, 13(4), 575–582.
Yegnenarayana, B., & Murthy, K. S. R. (2009). Event based instantaneous fundamental frequency estimation from speech signals. IEEE Transactions on Audio, Speech and Language Processing, 17(4), 614–624.
Zheng, N., Lee, T., & Ching, P. C. (2007). Integration of complimentary acoustic features for speaker recognition. IEEE Signal Processing Letters, 14(3), 181–184.
Zue, V., Seneff, S., & Glassa, J. (1990). Speech database development at MIT: Timit and beyond. Speech Communication, 9(4), 351–356.
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Pati, D., Prasanna, S.R.M. Subsegmental, segmental and suprasegmental processing of linear prediction residual for speaker information. Int J Speech Technol 14, 49–64 (2011). https://doi.org/10.1007/s10772-010-9087-8
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DOI: https://doi.org/10.1007/s10772-010-9087-8