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Erschienen in:
Historical and Procedural Overview of Forensic Speaker Recognition as a Science Kapitel lesen Erstes Kapitel lesen

2012 | OriginalPaper | Buchkapitel

13. Prosodic Features for Speaker Recognition

verfasst von : Leena Mary, Ph.D.

Erschienen in: Forensic Speaker Recognition

Verlag: Springer New York

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Abstract

In this chapter the effectiveness of syllable-based prosodic features for speaker recognition is discussed. The term prosody represents a collection of characteristics such as intonation, stress and timing, primarily expressed using variations in pitch, energy and duration at various levels of speech. Prosody reflects the learned/acquired speaking habits of a person and hence contributes for speaker recognition. Because prosodic features are less affected by channel mismatch and noise, they are particularly well suited for speaker forensics, a field that demands accurate identification of suspects with as few mitigating conditions as possible. In this chapter, the author describes a method for extracting prosodic features directly from speech signal. Applying this method, speech is segmented into syllable-like regions using vowel onset points (VOP). The locations of VOPs serve as reference for extraction and representation of prosodic features. The effectiveness of the prosodic features for speaker recognition is demonstrated for extended task of NIST speaker recognition evaluation 2003. Combining evidence from spectral features with that of the proposed prosodic features helps to improve overall speaker recognition accuracy.

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Vorheriges Kapitel Aerodynamic and Acoustic Theory of Voice Production
Nächstes Kapitel Speaker Identification Using Intermediate Matching Kernel-Based Support Vector Machines
Literatur
1.
2.
Doddington GG (2001) Speaker recognition based on idiolectic differences between speakers. Proc. EUROSPEECH, Aalborg, Denmark, pp 2521–2524
3.
Campbell JP (1997) Speaker recognition: a tutorial. Proc IEEE 85(9):1437–1462CrossRef
4.
Mary L (2006) Multilevel implicit features for language and speaker recognition. Ph. D. Thesis, Indian Institute of Technology, Madras
5.
Kinnunen T, Li H (2010) An overview of text-independent speaker recognition: from features to supervectors. Speech Commun 52:12–40CrossRef
6.
7.
Reynolds D, Andrews W, Campbell J, Navratil J, Peskin B, Adami A, Jin Q, Klusacek D, Abramson J, Mihaescu R, Godfrey J, Jones D, Xiang B (2003) The superSID project: exploiting high-level information for high-accuracy speaker recognition Proc. IEEE Int. Conf. Acoust., Speech and Signal Processing, Hong Kong, China, 4, pp 784–787
8.
Shriberg E, Stolcke A, Hakkani-Tur D, Tur G (2000) Prosody-based automatic segmentation of speech into sentences and topics. Speech Commun 32:127–154CrossRef
9.
Sonmez MK, Heck L, Weintraub M, Shriberg E (1997) A lognormal tied mixture model of pitch for prosody-based speaker recognition. Proc. EUROSPEECH, Rhodes, Greece. 3, pp 1391–1394
10.
Atkinson JE (1978) Correlation analysis of the physiological factors controlling fundamental voice frequency. J Acoust Soc Am 63(1):211–222CrossRef
11.
Yegnanarayana B, Prasanna SRM, Zachariah JM, Gupta CS (2005) Combining evidence from source, suprasegmental and spectral features for a fixed-text speaker verification system. IEEE Trans Speech Audio Process 13(4):575–582CrossRef
12.
Atal B (1972) Automatic speaker recognition based on pitch contours. J Acous Soc Am 52(3):1687–1697CrossRef
13.
Adami AG, Mihaescu R, Reynolds DA, Godfrey JJ (2003) Modeling prosodic dynamics for speaker recognition. Proc. ICASSP, Hong Kong, China, 4, pp 788–791
14.
Makhoul J (1975) Linear prediction: a tutorial review. Proc IEEE 63:561–580CrossRef
15.
Furui S (1981) Cepstral analysis technique for automatic speaker verification. IEEE Trans Speech Audio Process 29:254–272
16.
Reynolds DA, Rose R (1995) Robust text-independent speaker identification using Gaussian mixture speaker models. IEEE Trans Speech Audio Process 3:72–83CrossRef
17.
Reynolds DA (1996) The effect of handset variability on speaker recognition performance: Experiments on the switchboard corpus. Proc. ICASSP, Atlanta, GA, USA, 1, pp 113–116
18.
Thyme-Gobbel AE, Hutchins SE (1996) On using prosodic cues in automatic language identification. Proc. Int. Conf. Spoken Language Processing, Philadelphia, PA, USA, 3, pp 1768–1772
19.
Mary L, Yegnanarayana B (2008) Extraction and representation of prosodic features for language and speaker recognition. Speech Commun 50:782–796CrossRef
20.
Drygajlo A (2007) Forensic automatic speaker recognition. IEEE Signal Process Mag 132–135
21.
Shriberg E, Stolcke A (2008) The case for automatic higher level features in forensic speaker recognition. Proc. Interspeech, Brisbane, Australia, pp 1509–1512
22.
Rose P (2006) Technical speaker recognition: evaluation, types and testing of evidence. Comp Speech Lang 20:159–1914CrossRef
23.
Shriberg E, Ferrer L, Kajarekar S, Venkataraman A, Stolcke A (2005) Modeling prosodic feature sequences for speaker recognition. Speech Commun 46:455–472CrossRef
24.
Sonmez MK, Shriberg E, Heck L, Weintraub M (1998) Modeling dynamic prosodic variation for speaker variation. Proc. ICSLP, Sydney, Australia, 7, pp 3189–3192
25.
Adami AG, Mihaescu R, Reynolds DA, Godfrey JJ (2003) Modeling prosodic dynamics for speaker recognition. Proc. ICASSP, Hong kong, China, 4, pp 788–791
26.
Peskin B, Navratil J, Abramson J, Jones D, Klusacek D, Reynolds D, Xiang B (2003) Using prosodic and conversational features for high-performance speaker recognition: report from JHU WS`02. Proc. ICASSP, Hong kong, China, 4, pp 792–795
27.
Rouas J, Farinas J, Pellegrino F, Andre-Obrecht R (2005) Rhythmic unit extraction and modelling for automatic language identification. Speech Commun 47:436–456CrossRef
28.
Nagarajan T, Murthy HA (2006) Language identification using acoustic log-likelihoods of syllable-like units. Speech Commun 48:913–926CrossRef
29.
Dehak N, Kenny P, Dumouchel P (2007) Continuous prosodic features and formant modeling with joint factor analysis for speaker verification. Proc. of Interspeech, pp 1234–1237
30.
Mary L, Yegnanarayana B (2006) Prosodic features for speaker verification. Proc. of Interspeech, Pittsburgh, Pennsylvania, pp 917–920
31.
MacNeilage PF (1998) The frame/content theory of evolution of speech production. Behav Brain Sci 21:499–546
32.
Krakow RA (1999) Physiological organization of syllables: a review. J Phonetics 27:23–54CrossRef
33.
Atterer M, Ladd DR (2004) On the phonetics and phonology of “segmental anchoring” of F0: evidence from German. J Phonetics 32:177–197CrossRef
34.
Prasanna SRM, Gangashetty SV, Yegnanarayana B (2001) Significance of vowel onset point for speech analysis. Proc. Signal Proc. Com, Indian Institute of Science, pp. 81–88
35.
Prasanna SRM (2004) Event-based analysis of speech. Ph D Thesis, Indian Institute of Technology, Madras
36.
Prasanna SRM, Yegnanarayana B (2005) Detection of vowel onset point events using excitation source information, Proc. of Interspeech, pp 1133–1136
37.
Prasanna SRM, Zachariah JM (2002) Detection of vowel onset point in speech. Proc. IEEE Int Conf Acoust Speech, Signal Processing, Orlando, Fl, USA 4:4159
38.
Ananthapadmanabha TV (1978) Epoch extraction of voice speech. Ph. D. Thesis, Indian institute of Science, Bangalore
39.
Hess W (1983) Pitch determination of speech signals. Springer, BerlinCrossRef
40.
Ananthapadmanabha TV, Yegnanarayana B (1979) Epoch extraction fromlinear prediction residual for identification of closed glottis interval. IEEE Trans ASSP 27:309–319CrossRef
41.
Ananthapadmanabha TV, Yegnanarayana B (1975) Epoch extraction of voice speech. IEEE Trans ASSP 23:562–570CrossRef
42.
Taylor P (2000) Analysis and synthesis of intonation using the tilt model. J Acoust Soc Am 107(3):1697–1714CrossRef
43.
Gussenhoven C, Reepp BH, Rietveld A, Rump HH, Terken J (1997) The perceptual prominence of fundamental frequency peaks. J Acoust Soc Am 102(5):3009–3022CrossRef
44.
Yegnanarayana B (1999) Artificial neural network. Prentice Hall of India, New Delhi
45.
Yegnanarayana B, Kishore SP (2002) AANN-An alternative for GMM for pattern recognition. Neural Netw 15(3):459–469CrossRef
Metadaten
Titel
Prosodic Features for Speaker Recognition
verfasst von
Leena Mary, Ph.D.
Copyright-Jahr
2012
Verlag
Springer New York
Buch
Forensic Speaker Recognition

Print ISBN: 978-1-4614-0262-6

Electronic ISBN: 978-1-4614-0263-3

Copyright-Jahr: 2012

DOI
https://doi.org/10.1007/978-1-4614-0263-3_13

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