Skip to main content

Historical and Procedural Overview of Forensic Speaker Recognition as a Science

  • Chapter
  • First Online:
Forensic Speaker Recognition

Abstract

Forensic phonetics and acoustics are nowadays widely used regarding police and legal use of acoustic samples. Among many tasks included in this area, forensic speaker recognition is considered as one of the most complex problems. Forensic speaker recognition, sometimes called forensic speaker comparison, is a process for making judgments on whether or not two speech samples are from the same speaker. This chapter introduces the historical backgrounds of forensic speaker recognition including “voiceprint” controversy, human-based visual and auditory forensic speaker recognition, and automatic forensic speaker recognition. Procedural considerations in forensic speaker recognition processes and factors that affect recognition performances are also presented. Finally, we will give a summary of the progress and developments made in the forensic automatic speaker recognition.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Nolan F (1983) The phonetic basis of speaker recognition. Cambridge studies in speech science and communiation. Cambridge University Press, Cambridge

    Google Scholar 

  2. Schmidt-Nielsen A, Stern KR (1985) Identification of known voices as a function of familiarity and narrow-band coding. J Acoust Soc Am 77:658–663

    Article  Google Scholar 

  3. Van Lacker D, Kreiman J, Emmorey K (1985) Familiar voice recognition: patterns and parameters part 1: recognition of backward voices. J Phonetics 13:19–38

    Google Scholar 

  4. Van Lacker D, Kreiman J (1985) Familiar voice recognition: patterns and parameters part 2: recognition of rate-altered voices. J Phonetics 13:39–52

    Google Scholar 

  5. Cheney D, Seyfarth R (1980) Vocal recognition in free-ranging vervet monkeys. Anim Behav 28:362–367

    Article  Google Scholar 

  6. Rendall D, Rodman PS, Emond RE (1996) Vocal recognition of individuals and kin in free-ranging rhesus monkeys. Anim Behav 51:1007–1015

    Article  Google Scholar 

  7. Sugiura H (2001) Vocal exchange of coo calls in Japanese macaques. In: Matsuzawa T (ed) Primate origins of human cognition and behaviour. Springer, Tokyo, pp 135–154

    Google Scholar 

  8. Bricker P, Pruzansky S (1976) Speaker recognition. In: Lass N (ed) Contemporary issues in experimental phonetics. Academic Press, New York, pp 295–326

    Google Scholar 

  9. Furui S (1992) Acoustic and speech engineering (onkyo, onsei kougaku). Kindai Kagakusha Publishing Company, Tokyo

    Google Scholar 

  10. National Research Council (1979) On the theory and practice of voice identification. National Academy of Science, Washington, pp 3–13

    Google Scholar 

  11. Steinberg JC (1934) Application of sound measuring instruments to the study of phonetic problems. J Acoust Soc Am 6:16–24

    Article  Google Scholar 

  12. Potter R (1945) Visible patterns of speech. Science 102:463–470

    Article  Google Scholar 

  13. Grey CHG, Kopp GA (1944) Voiceprint identification. Bell Telephone Laboratory Annual Report, New York, pp 1–14

    Google Scholar 

  14. Tosi O, Oyer H, Lashbrook W, Pedrey C, Nicol J, Nash E (1972) Experiment on voice identification. J Acoust Soc Am 51:2030–2043

    Article  Google Scholar 

  15. Kersta L (1962) Voiceprint identification. Nature 196:1253–1257

    Article  Google Scholar 

  16. Campbell JP, Shen W, Campbell WM, Schwartz R, Bonastre JF, Matrouf D (2009) Forensic speaker recognition. IEEE Signal Process Mag 26:95–103

    Article  Google Scholar 

  17. Young MA, Campbell RA (1967) Effects of context on talker identification. J Acoust Soc Am 42:1250–1254

    Article  Google Scholar 

  18. Tosi O (1968) Speaker identification through acoustic spectrography. Proc Logoped Phoniatr, pp 138–145

    Google Scholar 

  19. Stevens KN, Williams CE, Carbonell JR, Woods B (1968) Speaker authentication and identification: a comparison of spectrographic and auditory presentations of speech material. J Acoust Soc Am 44:1596–1607

    Article  Google Scholar 

  20. Bolt RH, Cooper FS, David EE Jr, Denes PB, Pickett JM, Stevens KN (1970) Speaker identification by speech spectrograms: a scientists’ view of its reliability for legal purposes. J Acoust Soc Am 47:597–612

    Article  Google Scholar 

  21. Bolt RH, Cooper FS, David EE Jr, Denes PB, Pickett JM, Stevens KN (1973) Speaker identification by speech spectrograpms: some further observations. J Acoust Soc Am 54:531–534

    Article  Google Scholar 

  22. Koenig BE (1986) Spectrographic voice identification: a forensic survey. J Acoust Soc Am 79:2088–2090

    Article  Google Scholar 

  23. Shipp T, Doherty TE, Hollien H (1987) Some fundamental considerations regarding voice identification. J Acoust Soc Am 82:687–688

    Article  Google Scholar 

  24. Koenig BE, Ritenour DV Jr, Kohus BA, Kelly S (1987) Reply to ‘Some fundamental considerations regarding voice identification’. J Acoust Soc Am 82:688–689

    Article  Google Scholar 

  25. Lindh J (2004) Handling the voiceprint issue. Proc Fonetik, pp 72–75

    Google Scholar 

  26. Poza FT, Begault DR (2005) Voice identification and elimination using sural-spectrographic protocols. Proc AES Int’l Conf, pp 1–8

    Google Scholar 

  27. McGehee F (1937) The reliability of the identification of the human voice. J Gen Psychol 17:249–271

    Article  Google Scholar 

  28. McGehee F (1944) An experimental study of voice recognition. J Gen Psychol 31:53–65

    Article  Google Scholar 

  29. Pollack I, Pickett JM, Sumby WH (1954) On the identification of speaker by voice. J Acoust Soc Am 26:403–406

    Article  Google Scholar 

  30. Bricker P, Pruzansky S (1966) Effects of stimulus content and duration on talker identification. J Acoust Soc Am 40:1441–1450

    Article  Google Scholar 

  31. Clifford BR (1980) Voice identification by human listeners: on earwitness reliability. Law Human Behav 4:373–394

    Article  Google Scholar 

  32. Papcun G, Kreiman J, Davis A (1989) Long-term memory for unfamiliar voices. J Acoust Soc Am 85:913–925

    Article  Google Scholar 

  33. Yarmey AD, Matthys E (1992) Voice identification of an abductor. Appl Cogn Psychol 6:367–377

    Article  Google Scholar 

  34. Yarmey AD, Yarmey AL, Yarmey M, Parliament L (2001) Commonsense beliefs and the identification of familiar voices. Appl Cogn Psychol 15:283–299

    Article  Google Scholar 

  35. O’Shaughnessy D (2001) Speech communication—human and machine, 2nd edn. Addison-Wesley Publishing Company, New York

    Google Scholar 

  36. Hollien H (2002) Forensic voice identification. Academic Press, San Diego

    Google Scholar 

  37. Bonastre JF, Bimbot F, Boe LJ, Campbell JP, Reynolds DA, Magrin-Chagnolleau I (2003) Person authentication by voice: a need for caution. Proc Eurospeech, pp 1–4

    Google Scholar 

  38. Denes PB, Pinson EN (1993) The speech chain, 2nd edn. Worth Publishers, New York

    Google Scholar 

  39. Kuenzel H (2000) Effects of voice disguise on speaking fundamental frequency. Forensic Ling 7:149–179

    Article  Google Scholar 

  40. Zhang C, Tan T (2007) Voice disguise and automatic speaker recognition. Forensic Sci Int 175:118–122

    Article  Google Scholar 

  41. Reich AR, Duke JE (1979) Effects of selected vocal disguises upon speaker identification by listening. J Acoust Soc Am 66:1023–1028

    Article  Google Scholar 

  42. Orchard TL, Yarmey AD (1995) The effects of whispers, voice-sample duration, and voice distinctiveness on criminal speaker identification. Appl Cogn Psychol 9:249–260

    Article  Google Scholar 

  43. Sjoestroem M, Eriksson E, Zetterholm E, Sullivan KP (2006) A switch of dialect as disguise. Lund Univ. Linguistics and Phonetics Woking Papers, vol 52, pp 113–116

    Google Scholar 

  44. Markham D (1999) Listeners and disguised voices: the imitation and perception of dialect accent. J Speech Lang Law 6:289–299

    Google Scholar 

  45. Amino K, Arai T (2009) Dialectal characteristics of Osaka and Tokyo Japanese: analyses of phonologically identical words. Proc Interspeech, pp 2303–2306

    Google Scholar 

  46. House AS, Stevens KN (1993) Speech production: thirty years after. J Acoust Soc Am 94:1763

    Article  Google Scholar 

  47. Hollien H, Schwartz R (2000) Aural-perceptual speaker identification: problems with noncontemporary samples. Forensic Linguist 7:199–211

    Article  Google Scholar 

  48. Hollien H, Schwartz R (2001) Speaker identification utilizing noncontemporary speech. J Forensic Sci 46:63–67

    Google Scholar 

  49. Amino K, Osanai T, Kamada T, Makinae H, Arai T (2011) Effects of the phonological contents and transmission channels on forensic speaker recognition. In: Neustein A, Patil HA (eds) Advances in forensic speaker recognition. Springer

    Google Scholar 

  50. Kuenzel HJ (2001) Beware of the ’telephone effect’: the influence of telephone transmission on the measurement of formant frequencies. Forensic Liguist 8:80–99

    Article  Google Scholar 

  51. Byne C, Foulkes P (2004) The ‘mobile phone effect’ on vowel formants. J Speech Lang Law 11:1350–1771

    Google Scholar 

  52. Lawrence S, Nolan F, McDougall K (2008) Acoustic and perceptual effects of telephone transmission on vowel quality. J Speech Lang Law 15:161–192

    Google Scholar 

  53. Titze I (1989) Physiologic and acoustic differences between male and female voices. J Acoust Soc Am 85:1699–1707

    Article  Google Scholar 

  54. Kent RD, Read C (2001) Acoustic analysis of speech, 2nd edn. Cengage Learning

    Google Scholar 

  55. Clarke FR, Becker RW (1969) Comparison of techniques for discriminating among talkers. J Speech Hear Res 12:747–761

    Google Scholar 

  56. Thompson CP (1987) A language effect in voice identification. Appl Cogn Psychol 1:121–131

    Article  Google Scholar 

  57. Goggin J, Thompson CP, Strube G, Simental LR (1991) The role of language familiarity in voice identification. Mem Cognit 19:448–458

    Article  Google Scholar 

  58. Koester O, Schiller NO (1997) Different influences of the native language of a listener on speaker recognition. Forensic Linguist 4:18–28

    Google Scholar 

  59. Philippon AC, Cherryman J, Bull R, Vrij A (2007) Earwitness identification performances: the effect of language, target, deliberate strategies and indirect measures. Appl Cogn Psychol 21:539–550

    Article  Google Scholar 

  60. Hashimoto M, Kitagawa S, Higuchi N (1998) Quantitative analysis of acoustic features affecting speaker identification. J Acoust Soc Jpn 54:169–178

    Google Scholar 

  61. Hollien H, Majewski W, Doherty TE (1982) Perceptual identification of voices under normal, stress, and disguise speaking conditions. J Phonetics 10:139–148

    Google Scholar 

  62. Ladefoged P, Ladefoged J (1980) The ability of listeners to identify voices. UCLA Working Papers Phon 49:43–89

    Google Scholar 

  63. Nygaard L (2005) Perceptual integration of linguistic and nonlinguistic properties of speech. In: Pisoni DB, Remez RE (eds) The handbook of speech perception. Blackwell, Oxford, pp 390–413

    Google Scholar 

  64. Roebuck R, Wilding J (1993) Effects of vowel variety and sample length on identification of a speaker in a line-up. Appl Cogn Psychol 7:475–481

    Article  Google Scholar 

  65. Cook S, Wilding J (1997) Earwitness testimony: never mind the variety, hear the length. Appl Cogn Psychol 11:95–111

    Article  Google Scholar 

  66. Loftus EF, Loftus GR, Messo J (1987) Some facts about weapon focus. Law Human Behav 11:55–62

    Article  Google Scholar 

  67. Loftus EF, Miller DG, Burns HJ (1978) Semantic integration of verbal information into a visual memory. J Exp Psychol Human Learn Mem 4:19–31

    Article  Google Scholar 

  68. Schooler JW, Engstler-Schooler TY (1990) Verbal overshadowing of visual memories: some things are better left unsaid. Cogn Psychol 22:36–71

    Article  Google Scholar 

  69. Chin JM, Schooler JW (2008) Why do words hurt? Content, process, and criterion shift accounts of verbal overshadowing. Eur J Cogn Psychol 20:396–413

    Article  Google Scholar 

  70. Kitagami S (2001) Disruptive effect of verbal encoding on memory and cognition of nonverbal information. Kyoto Univ Dept Edu Bull Paper 47:403–413

    Google Scholar 

  71. Kasahara H, Ochi K (2008) Verbal overshadowing effect in earwitness perception. Proc Ann Conv Jpn Psychol Assoc 72:889

    Google Scholar 

  72. Cook S, Wilding J (2001) Earwitness testimony: effects of exposure and attention on the face overshadowing effect. Br J Psychol 92:617–629

    Article  Google Scholar 

  73. Kasahara H, Ochi K (2006) Effect of face presence on memory for a voice. J Jpn Acad Facial Studies 6:71–76

    Google Scholar 

  74. Yarmey AD, Yarmey AL, Yarmey MJ (1994) Face and voice identifications in showups and lineups. Appl Cogn Psychol 8:453–464

    Article  Google Scholar 

  75. Bull R, Clifford BR (1984) Earwitness voice recognition accuracy. In: Wells GL, Loftus EF (eds) Eyewitness testimony: psychological perspectives. Cambridge University Press, Cambridge, pp 92–123

    Google Scholar 

  76. Kerstholt JH, Jansen N, Van Amelsvoort AG, Broeders AP (2004) Earwitnesses: effects of speech duration, retention, internal and acoustic environment. Appl Cogn Psychol 18:327–336

    Article  Google Scholar 

  77. Van Wallendael LR, Surace A, Parsons DH, Brown M (1994) Earwitness’ voice recognition: factors affecting accuracy and impact on jurors. Appl Cogn Psychol 8:661–677

    Article  Google Scholar 

  78. Pruzansky S (1963) Pattern-matching procedure for automatic talker recognition. J Acoust Soc Am 35:354–358

    Article  Google Scholar 

  79. Li KP, Dammann JE, Chapman WD (1966) Experimental studies in speaker verification, using and adaptive system. J Acoust Soc Am 40:966–978

    Article  Google Scholar 

  80. Glenn JW, Kleiner N (1967) Speaker identification based on nasal phonation. J Acoust Soc Am 43:368–372

    Article  Google Scholar 

  81. Furui S, Itakura F, Saito S (1972) Talker recognition by the longtime averaged speech spectrum. IEICE Trans 55-A(1):549–556

    Google Scholar 

  82. Wolf JJ (1971) Efficient acoustic parameters for speaker recognition. J Acoust Soc Am 51:2044–2056

    Article  Google Scholar 

  83. Atal BS (1972) Automatic speaker recognition based on pitch contours. J Acoust Soc Am 52:1687–1697

    Article  Google Scholar 

  84. Furui S, Itakura F (1973) Talker recognition by statistical features of speech sounds. Electron Commun Jap 56-A:62–71

    Google Scholar 

  85. Atal BS (1974) Effectiveness of linear prediction characteristics of the speech wave for automatic speaker identification and verification. J Acoust Soc Am 55:1304–1312

    Article  Google Scholar 

  86. Sambur MR (1975) Selection of acoustic features for speaker identification. IEEE Trans Acoust Speech Sig Process 23:176–182

    Article  Google Scholar 

  87. Hollien H, Majewski W (1977) Speaker identification by long-term spectra under normal and distorted speech conditions. J Acoust Soc Am 62:975–980

    Article  Google Scholar 

  88. Matsumoto H, Nimura T (1978) Text-independent speaker identification based on piecewise canonical discriminant analysis. Proc Int Conf Acoust Speech Sig Process, 3:291–294

    Google Scholar 

  89. Markel JD, Davis SB (1979) Text-independent speaker recognition from a large linguistically unconstrained time spaced data base. IEEE Trans Acoust Speech Sig Process 27:74–82

    Article  Google Scholar 

  90. Furui S (1981) Cepstral analysis technique for automatic speaker verification. IEEE Trans Acoust Speech Sig Process 29:254–272

    Article  Google Scholar 

  91. Li KP, Wrench EH (1983) Text-independent speaker recognition with short utterances. Proc Int Conf Acoust Speech Sig Process, 8:555–558

    Google Scholar 

  92. Soong F, Rosenberg A, Rabiner L, Juang BH (1985) A vector quantization approach to speaker recognition. Proc Int Conf Acoust Speech Sig Process, 387–390

    Google Scholar 

  93. Rosenberg A, Soong F (1986) Evaluation of a vector quantisation talker recognition system in text independent and text dependent modes. Proc Int Conf Acoust Speech Sig Process, 11:873–876

    Google Scholar 

  94. Shirai K, Mano K, Ishige D (1987) Speaker identification based on frequency distribution of vector-quantised spectra. IEICE Trans 70-D:1181–1188

    Google Scholar 

  95. Rosenberg A, Lee CH, Soong F (1990) Sub-word unit talker verification using Hidden Markov Models. Proc Int Conf Acoust Speech Sig Process, 1:269–272

    Google Scholar 

  96. Higgins A, Bahler L, Porter J (1991) Speaker verification using randomized phrase prompting. Digit Signal Process 1:89–106

    Google Scholar 

  97. Tishby NZ (1991) On the application of mixture AR Hidden Markov Models to text-independent speaker recognition. IEEE Trans Acoust Speech Sig Process 39:563–570

    Google Scholar 

  98. Reynolds AD, Carlson B (1995) Text-dependent speaker verification using decoupled and integrated speaker and speech recognizers. Proc Eurospeech, pp 647–650

    Google Scholar 

  99. Reynolds AD, Rose R (1995) Robust text-independent speaker identification using Gaussian mixture speaker models. IEEE Trans Speech Audi Process 3:72–83

    Article  Google Scholar 

  100. Che C, Lin Q (1995) Speaker recognition using HMM with experiments on the YOHO database. Proc Eurospeech, pp 625–628

    Google Scholar 

  101. NIST webpage. http://www.nist.gov/index.html

    Google Scholar 

  102. NIST-SRE. http://www.itl.nist.gov/iad/mig//tests/sre/

    Google Scholar 

  103. Doddington GR, Przybocki MA, Martin AF, Reynolds DA (2000) The NIST speaker recognition evaluation—overview, methodology, systems, results, perspective. Speech Commun 31:225–254

    Article  Google Scholar 

  104. Nakasone H, Beck SD (2001) Forensic automatic speaker recognition. Proc A Speaker Odyssey—the speaker recognition workshop, pp 139–142

    Google Scholar 

  105. Drygajlo A (2007) Forensic automatic speaker recognition. IEEE Signal Process Mag 24:132–135

    Article  Google Scholar 

  106. Martin A, Doddington G, Kamm T, Ordowski M, Przybocki M (1997) The DET curve in assessment of detection task performance. Proc Eurospeech, pp 1895–1898

    Google Scholar 

  107. Bimbot F, Bonastre JF, Fredouille C, Gravier G, Magrin-Chagnolleau I, Meignier S, Merlin T, Ortega-Garcia J, Petrovska-Delacretaz D, Reynolds DA (2004) A tutorial on text-independent speaker verification. EURASIP J Appl Signal Process 4:430–451

    Google Scholar 

  108. Noda H, Darada K, Kawaguchi E, Sawai H (1998) A context-dependent approach for speaker verification using sequential decision. Proc Int Conf Spoken Lang Process

    Google Scholar 

  109. Ortega-Garcia J, Cruz-Llanas S, Gonzalez-Rodriguez J (1998) Quantitative influence of speech variability factors for automatic speaker verification in forensic tasks. Proc Int Conf Spoken Lang Process

    Google Scholar 

  110. Gonzalez-Rodriguez J, Ortega-Garcia J, Lucena-Molina JJ (2001) On the application of the Bayesian approach to real forensic conditions with GMM-based systems. Proc a speaker odyssey—the speaker recognition workshop, pp 135–138

    Google Scholar 

  111. Meuwly D, Drygajlo A (2001) Forensic speaker recognition based on a Bayesian framework and Gaussian Mixture Modelling (GMM). Proc a speaker odyssey—the speaker recognition workshop, pp 145–150

    Google Scholar 

  112. Alexander A, Botti F, Drygajlo A (2004) Handling mismatch in corpus-based forensic speaker recognition. Proc odyssey04 the speaker and language recognition workshop, pp 69–74

    Google Scholar 

  113. Ramos D, Gonzalez-Rodriguez J, Gonzalez-Dominguez J, Lucena-Molina JJ (2008) Addressing database mismatch in forensic speaker recognition with Ahumada III: A public real-casework database in Spanish Proc Interspeech, pp 1493–1496

    Google Scholar 

  114. Thiruvaran T, Ambikairajah E, Epps J (2008) FM features for automatic forensic speaker recognition. Proc Interspeech, pp 1497–1500

    Google Scholar 

  115. Becker T, Jessen M, Grigoras C (2008) Forensic speaker verification using formant features and Gaussian Mixture Models. Proc Interspeech, pp 1505–1508

    Google Scholar 

  116. Becker T, Jessen M, Alsbach S, Bross F, Meier T (2010) SPES: The BKA forensic automatic voice comparison system. Proc Odyssey—the Speaker and Language Recognition Workshop, pp 58–62

    Google Scholar 

  117. Hermansky H (1989) Perceptual linear predictive (PLP) analysis of speech. J Acoust Soc Am 87:1738–1752

    Article  Google Scholar 

  118. Paul JE, Rabinowitz AS, Riganati JP, Richardson JM (1975) Semi-automatic speaker identification system (SASIS)—analytical studies. Final Report C74–11841501, Rockwell International

    Google Scholar 

  119. Bunge E (1977) Speaker recognition by computer. Philips Tech. Review 37(8):207–219

    Google Scholar 

  120. Nakasone H, Melvin C (1989) C.A.V.I.S.: (Computer assisted voice identification system). Final Report 85-IJ-CX-0024. National Institute of Justice

    Google Scholar 

  121. Falcone M, De Sairo N (1994) A PC speaker identification system for forensic use: IDEM. Proc ESCA workshop on automatic speaker recognition, identification and verification, pp 169–172

    Google Scholar 

  122. Gonzalez-Rodriguez J, Ortega-Garcia J, Lucena-Molina JJ (2001) IdentiVox: a PC-Windows tool for text-independent speaker recognition in forensic environments. Prob Forensic Sci 47:246–253

    Google Scholar 

  123. Drygajlo A, Meuwly D, Alexander A (2003) Statistical methods and Bayesian interpretation of evidence in forensic automatic speaker recognition. Proc Eurospeech, pp 689–692

    Google Scholar 

  124. Agnitio, Sociedad Limitada. http://www.agnitio.es/index.php

    Google Scholar 

  125. Morrison GS (2009) Forensic voice comparison and the paradigm shift. Sci Justice 49:298–308

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kanae Amino Ph.D. .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Amino, K., Osanai, T., Kamada, T., Makinae, H., Arai, T. (2012). Historical and Procedural Overview of Forensic Speaker Recognition as a Science. In: Neustein, A., Patil, H. (eds) Forensic Speaker Recognition. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0263-3_1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-0263-3_1

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-0262-6

  • Online ISBN: 978-1-4614-0263-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics