OSAC 2023-N-0023 Standard Guide to the Forensic 

Speaker Recognition Landscape 

1 

OSAC 2023-N-0023 

Standard Guide to Forensic 

Speaker Recognition 

Landscape 

Speaker Recognition Subcommittee 

Digital/Multimedia Scientific Area Committee 

Organization of Scientific Area Committees (OSAC) for Forensic Science 
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OSAC 2023-N-0023 Standard Guide to the Forensic 

Speaker Recognition Landscape 

DRAFT OSAC Proposed Standard 

OSAC 2023-N-0023 

Standard Guide to the Forensic 

Speaker Recognition Landscape 

Prepared by 

Speaker Recognition Subcommittee 

Version 1.0 

September 2023 

Disclaimer 

This OSAC Proposed Standard was written by the [insert subcommittee or other unit name] of the 

Organization of Scientific Area Committees (OSAC) for Forensic Science following a process that 

includes an open comment period. This Proposed Standard will be submitted to a standards 

developing organization and is subject to change. 

There may be references in an OSAC Proposed Standard to other publications under development 

by OSAC. The information in the Proposed Standard, and underlying concepts and methodologies, 

may be used by the forensic-science community before the completion of such companion 

publications. 

Any identification of commercial equipment, instruments, or materials in the Proposed Standard 

is not a recommendation or endorsement by the U.S. Government and does not imply that the 

equipment, instruments, or materials are necessarily the best available for the purpose. 

 
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OSAC 2023-N-0023 Standard Guide to the Forensic 

Speaker Recognition Landscape 

Acknowledgements 

The Speaker Recognition Subcommittee thanks the following contributing authors who have 

provided substantial language to the document on multiple occasions: Sandra Ferrari Disner, 

Michael Jessen, and Finnian Kelly; as well as the following reviewers who provided valuable and 

feedback on content: Anil Alexander, John Hansen, Larry Kincaid, David Marks, Stephanie 

Domitrovich, and Jennifer Stathakis. The Subcommittee also thanks Erica Gold who has been the 

editor through the document’s many revisions as well as lead author. 
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OSAC 2023-N-0023 Standard Guide to the Forensic 

Speaker Recognition Landscape 

Table of Contents 

1. Scope Page 5 

2. Referenced Documents Page 5 

3. Terminology Page 5 

4. Summary of Practice Page 5 

5. Significance of Use Page 6 

6. Procedure Page 8 

7. Annex: Bibliography Page 11 

 
OSAC 2023-N-0023 Standard Guide to the Forensic 

Speaker Recognition Landscape 

1 

1 1. Scope 

2 Forensic speaker recognition, also referred to or covered by the terms forensic speech science, 

3 forensic phonetics, and speaker identification, aims to determine whether speakers are likely 

4 to be the same person or different people from at least two recordings (e.g., known and 

5 questioned recordings). This document provides a landscape of the methods used for analysis 

6 in the field of speaker recognition as well as the commonly used interpretation frameworks – 

7 Conclusion Frameworks. This document also establishes that the wider speaker recognition 

8 community has rejected previously held beliefs regarding the scientific validity of 

9 voiceprinting. This document is intended to serve as a general overview and reference (as it is 

10 currently practiced in the field) for forensic speaker recognition. 

11 

12 2. Referenced Documents 

13 2.1 OSAC Standards.

14 2.2 Organization of Scientific Area Committees (OSAC) for Forensic Science Speaker 

15 Recognition Subcommittee, “Essential scientific literature for human-supervised automatic 

approaches to forensic speaker recognition.”1 16 

17 

18 3. Terminology 

19 

20 For purposes of this document, the following definitions and acronyms apply. 

21 3.1 Definition of terms specific to this standard. 

22 

23 3.2 Automatic Speaker Recognition (ASR), n. as used in this guideline, ASR requires the 

24 use of specialized software to compare speech samples, producing a numerical score that is 

25 evaluated from the perspective of the same-speaker origin as well as the different-speaker 

26 origin. 

27 

28 4. Summary of Practice 

29 

30 4.1 The main objective of forensic speaker recognition is to determine whether speakers are 

31 likely to be the same or different. Forensic practitioners are typically presented with a 

32 minimum of two audio recordings and asked to carry out an analysis of those audio recordings. 

33 The methods used for analysis in forensic speaker recognition have evolved far past previous 

34 traditions of voiceprinting, which has been rejected and discredited by the speaker recognition 

35 community. Methods of analysis that are currently in practice include: auditory phonetic 

36 analysis, acoustic phonetic analysis, semi-automatic acoustic analysis, automatic speaker 

37 recognition, human-assisted speaker recognition, and combined human and automatic speaker 

38 recognition analysis. 

39 

1 Prepared by Scientific Literature Working Group, Forensic Speaker Recognition Subcommittee, “Essential scientific literature for humansupervised automatic approaches to forensic speaker recognition,” Organization of Scientific Area Committees (OSAC), Online, Available: 

https://www.nist.gov/document/essentialscientificliteratureforhuman
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OSAC 2023-N-0023 Standard Guide to the Forensic 

Speaker Recognition Landscape 

40 Indeed, just as there are multiple methods for analysis being implemented in speaker 

41 recognition, there are also a number of different conclusion frameworks that have also been 

42 adopted. Interpretation frameworks (or Conclusion Frameworks) that are currently being 

43 utilized by the speaker recognition community include: the binary decision, probability scales, 

44 likelihood ratios (both verbal and numerical), the UK Position Statement, and support 

45 statements. 

46 

47 5. Significance of Use 

48 

49 5.1 Introduction: Forensic speaker recognition involves the comparison of at least two speech 

50 samples (typically from a questioned and known recording to determine whether speakers are 

51 likely to be the same or different. It is common for forensic speaker recognition to be referred 

52 to by a few other terms, largely dictated by the field in which the subject is researched and 

53 practiced. Within the forensic speech science and forensic phonetics communities, the task is 

54 often referred to as forensic speaker comparison or forensic voice comparison. Within the 

55 engineering communities, forensic speaker recognition is also sometimes referred to as 

56 forensic speaker identification. For clarification purposes, the task of comparing speech 

57 samples is referred to in this document as forensic speaker recognition. This document is 

58 intended to provide a general overview of forensic speaker recognition. For more detailed 

59 information about the topics discussed in this document, please see the OSAC Speaker 

Recognition Essential Literature document.2 60 

61 

62 5.2 The objective of the forensic practitioner carrying out forensic speaker recognition is to 

63 provide the trier(s) of fact with an informed opinion regarding the probability of obtaining the 

64 evidence (under the hypothesis that the samples came from the same person, versus under the 

65 hypothesis that two different speakers produced each sample). This objective can be reached 

66 by practitioners using a variety of methods (i.e., auditory phonetic and acoustic phonetic 

67 analysis, semi-automatic acoustic analysis, automatic speaker recognition, human-assisted 

68 speaker recognition, or combined human and automatic speaker recognition analysis). While 

69 questioned recordings often involve an array of different sounds and speech, the task of 

70 forensic speaker recognition is wholly concerned with the speech (and sounds) produced by 

71 individuals. Those sounds that cannot be attributed to a person are outside the scope of forensic 

72 speaker recognition, and fall more into the general area of audio or acoustic forensics. The aim 

73 of this document is not to promote or suggest any one method of analysis or interpretation 

74 framework over another, but rather to provide a general landscape of the methods used within 

75 the speaker recognition community.

76 

77 5.3 Voiceprinting: While there are many ways to conduct forensic speaker recognition, it is 

78 important to note here that the method known as "voiceprinting" is not supported by the 

scientific community, and has been discredited. The term "voiceprint2 79 " was coined by the 

80 author of an article (Kersta 1962) which appeared over a half-century ago. The name chosen 

81 for that methodology quite transparently implied parallels between a (never-proven) "theory 

82 of invariant speech" and the relative invariance of fingerprints. 

83 

84 

2 Gray and Kopp (1944) also used the term voiceprint with the same definition, however, they used the term with a space between the words voice 

and print. For all intents and purposes this document uses the term voiceprint without a space. 
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OSAC 2023-N-0023 Standard Guide to the Forensic 

Speaker Recognition Landscape 

85 5.3.1 The so-called "voiceprints" were the product of sound spectrography, a technology 

86 carried forward even to the present day, which is still of great utility to speech scientists. 

87 The most notable scientific failing of the voiceprint method was that it did not provide 

88 examiners with the vocal output (i.e., the audio). This inevitably obscured the phonetic 

89 nature of the patterns of acoustic energy and reduced the analysis to a simple pattern90 matching exercise. Nevertheless, that exercise was initially heralded with outsized claims 

91 of success. The article that introduced the voiceprinting method in 1962 reported that 

phonetically naïve examiners3 92 were able to identify a target voice with 99% accuracy, 

93 even from a pool of a dozen speakers. Not surprisingly, this new methodology soon caught 

94 the attention of law enforcement, and was presented as evidence in a number of criminal 

95 prosecutions, in the US and elsewhere. 

96 

97 5.3.2 However, the scientific community remained skeptical. Well-known phoneticians 

98 such as Peter Ladefoged and Harry Hollien reported that mere pattern matching (which is 

99 all that the young voiceprint examiners were asked to do) was incapable of yielding the 

100 astonishing results reported in the 1962 article. Due to the variability present in speech 

101 productions from sample to sample, spectrographic template matching is not effective and 

102 it is inconsistent in speaker recognition work. In time, phoneticians began to provide 

103 expert testimony against the admissibility of voiceprint evidence, and in consequence, a 

104 number of lower-court convictions were eventually overturned. In response to these 

105 criticisms, another academic linguist, Oscar Tosi, initiated a more rigorous, and 

106 procedurally transparent, voiceprint study (Tosi et al. 1972). This yielded less vertiginous, 

107 but more scientifically reliable results – 6% false identification errors and 13% false 

108 elimination errors, under laboratory conditions. Still, given the high stakes of introducing 

109 a still largely unsupported procedure into courts of law, a report issued by the National 

Research Council4 110 concluded that the voiceprint method lacked an adequate scientific 

111 basis for estimating reliability in many practical situations, pointing out in addition that 

112 laboratory evaluations of the voiceprint method showed increasing errors as the conditions 

113 for evaluation moved toward real-life situations, such as poor signal-to-noise ratios and 

114 dissimilar recording conditions. 

115 

116 5.3.3 The Federal Rules of Evidence, adopted in 1975, further challenged the voiceprint 

117 methodology by shifting the standards for admissibility in favor of practitioners whose 

118 "scientific, technical, and other specialized knowledge" can help the trier of fact "to 

understand the evidence or to determine a fact in issue"5 119 Phonetically untrained voiceprint 

120 examiners, who sought to identify speakers simply by looking at pictures of their voice 

121 signals, were left at a marked disadvantage. 

122 

123 

3 To drive home his point, Kersta used high school students, who had been given only one week of training, as his examiners. The difficulty of 

the task was augmented by a forced-choice design; "not sure" was not an option. 

4 National Research Council. 1979. On the Theory and Practice of Voice Identification. Washington, DC: The National Academies Press. 

https://doi.org/10.17226/19814. 

5 Federal Rules of Evidence 702 
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OSAC 2023-N-0023 Standard Guide to the Forensic 

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124 5.3.4 For further overviews of the voiceprint approach one can consult (this is not 

125 intended to be an exhaustive list, but merely a few selected references): 

126 

127 5.3.4.1 Hollien (1990) 

128 5.3.4.2 Hollien (2002) 

129 5.3.4.3 Rose (2002) 

130 5.4 While voiceprinting has been discredited from the speaker recognition community, it has 

131 also been declared inadmissible in court (for further information see U.S. v. Angleton 2003). 

132 

133 5.5 Additional methods that are properly applied and under certain circumstances are 

134 recognized as appropriate by the community are, in turn, described in detail below. It will 

135 become apparent that these methods have historically developed in parallel to one another, as 

136 they have grown out of different disciplines. However, it is not uncommon to see some cross137 over between the various methods used in forensic speaker recognition. This will be explained 

138 further in the sections that follow. 

139 

140 6. Procedure 

141 6.1 There is no one, single method that is used by all practitioners of forensic speaker 

142 recognition, and it is sometimes the case that some of these methods are combined when 

143 undertaking analysis. The methods most commonly employed in forensic speaker recognition 

144 are: auditory phonetic analysis, acoustic phonetic analysis, auditory phonetic + acoustic 

145 phonetic analysis, semi-automatic acoustic speaker recognition, automatic speaker 

146 recognition, human-assisted automatic speaker recognition, and a combination of auditory 

147 phonetic + acoustic phonetic analysis and (human-assisted) automatic speaker recognition. All 

148 seven approaches to speaker recognition are detailed below. 

149 

150 6.2 Auditory Phonetic Analysis (AuPA): AuPA is defined as the process by which “the expert 

151 listens analytically to the speech samples and attends to aspects of speech at the segmental and 

152 suprasegmental levels” (Gold and French 2011). AuPA is very important in the identification 

153 of language varieties, such as regional accent, foreign accent or in the detection of linguistic 

154 correlates of various social factors. Age, sex, and gender also fall into the category of 

155 characteristics most commonly judged auditorily. All of these can be classified as “group-level 

156 characteristics,” in contrast to “individual-level characteristics” (Hughes and Rhodes 2018). 

157 Group-level speaker characteristics are crucial in speaker profiling, but they also have their 

158 established place in speaker recognition. They can be important in defining the relevant 

159 population. They are also particularly powerful as evidence speaking against speaker identity: 

160 if, for example, the known and the unknown voices use two different regional varieties, it is 

161 likely that they are different individuals, given that bi-dialectalism is relatively rare. Group162 level characteristics can also provide important information that supports inclusion, within the 

163 context of the case. They are also particularly helpful in excluding speakers. Part of the 
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OSAC 2023-N-0023 Standard Guide to the Forensic 

Speaker Recognition Landscape 

164 tradition of AuPA has been to narrow down dialect to a point that it achieves the status of a 

165 rare combination of linguistic parameters only used by a few individuals (and ideally, however, 

166 rather unrealistic, just one individual). This can occur when a speaker uses only some features 

167 of a dialect (or other language variety) to the exclusion of others, or if features from various 

168 language varieties are combined. Discussions of these aspects related to language variety are 

169 provided in Jessen (2010; 2021) and Hughes & Rhodes (2018). 

170 

171 6.2.1 AuPA is also used for the description and interpretation of various individual-level 

172 speaker characteristics. According to the survey by Gold and French (2011), voice quality 

173 is a particularly important one. Voice quality can be measured acoustically (Keating et al. 

174 2015), but given the acoustic limitations typically encountered in forensic casework, most 

175 of these methods suffer from information loss or lack of applicability. Auditory analysis, 

176 instead, offers more robustness, though auditory analysis is also more subjective. Auditory 

177 voice quality assessment in forensics often builds upon the classificatory framework of 

178 the phonetician John Laver (1980), particularly in Europe. A description of that 

179 framework and how it is adapted to forensics is found in San Segundo et al. (2019), and a 

180 complete definition of voice quality can be found in McIntyre et al. (2021). Another 

181 classification framework for AuPA-based analysis has been developed for disfluency 

182 patterns, which include silent pauses, breathing pauses, filled pauses (utterance such as 

183 uh, and um) or sentence interruptions (McDougall and Duckworth 2018, de Boer and 

184 Heeren 2019, Hughes et al. 2016). Further speaker characteristics observed auditorily are 

185 listed in Gold and French (2011). 

186 

187 6.3 Acoustic Phonetic Analysis (AcPA): AcPA is the method by which “the expert analyzes 

188 and quantifies physical parameters of the speech signal using computer software. As with 

189 AuPA, this is labor intensive, involving a high degree of human input and judgment” (Gold 

190 and French 2011). AcPA traditionally has its strongest focus on speaker characteristics that 

191 have an anatomical motivation and that have been known since the 1950s to vary between 

192 women, men, and children, but also between individuals within these larger speaker categories. 

193 This applies to fundamental frequency and formant frequencies. 

194 

195 6.3.1 Fundamental frequency (f0), which is the frequency of the vibration patterns of the 

196 vocal folds, depends on the size of the larynx (especially vocal fold length), but it is to a 

197 degree controllable for linguistic purposes. As a way of disregarding locally determined 

198 linguistic factors, a common method is to average f0 (mean, mode, or median) across long 

199 utterances or the entire recording (Hudson et al. 2007). In this process, further irrelevant 

200 factors that have a strong influence on f0 must be controlled as much as possible; this is 

201 particularly important for the f0-raising effect of vocal effort (that is, speaking loudly) 

202 (Jessen et al. 2005). Speakers can also differ habitually in terms of how “melodically” 

203 they speak (scale from speaking monotonously to highly modulated). Standard deviation 

204 of f0 across long passages or the entire recording is a way of capturing these habitual 

205 speaker differences. Since mean and standard deviation of f0 are to some extent correlated, 

206 variability is sometimes expressed by the coefficient of variation (standard deviation 

207 divided by mean), by means of which the correlation almost disappears (Jessen et al. 

208 2005). 
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OSAC 2023-N-0023 Standard Guide to the Forensic 

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209 6.3.2 Vowel formant frequencies, which are characteristic patterns of amplitude peaks in 

210 the speech spectrum, are associated with the length of the speaker's vocal tract and other 

211 anatomical features. However, formant frequencies – especially the first formant (F1) and 

212 the second formant (F2) – are also crucial carriers of linguistic information; they are the 

213 main correlates of vowel distinctions in a language, and transitions between successive 

214 vowels serve to distinguish any intervening consonants. There is thus a clear need to 

215 control for these linguistic factors. One way, which is analogous to the processing of f0, 

216 is to average all the formants across long stretches of speech. This method is referred to 

217 as long-term formant analysis (Nolan and Grigoras 2005). Another method is to measure 

218 formant frequencies separately for different vowels. This is the traditional way formants 

219 are measured in phonetics. Beyond anatomical restrictions, there are degrees of freedom 

220 in transitioning from one target sound to the next. Hence, measuring formant dynamics is 

221 a third way of capturing formant information in forensic speaker recognition. It has been 

222 shown in many studies that when formant measurements are not limited to targets but, 

223 rather, when one takes into account the entire dynamics of the formant movements, 

224 speaker recognition capability is improved (see McDougall 2006 and Morrison 2009 for 

225 some of the early studies). 

226 

227 6.3.3 There are other speaker characteristics that can be based upon acoustic phonetic 

228 analysis (Gold and French 2011). For example, it is possible to measure the spectral 

229 energy distribution in fricatives or nasals (Kavanagh 2012) or to make temporal 

230 measurements in the domain of rhythm and timing (Dellwo et al 2015; Plug et. al. 2021). 

231 But most actual AcPA casework utilizes f0 and formants. 

232 

233 6.4 Auditory Phonetic + Acoustic Phonetic Analysis (AuPA+AcPA): AuPA+AcPA is the 

234 combination of both auditory and acoustic analysis in speaker recognition as detailed in §6.2 

235 and §6.3. The combination of AuPA and AcPA has also been referred to as an “auditory236 acoustic-phonetic” method that is carried out by forensic practitioners (Morrison et al. 2016).” 

237 The term “auditory-acoustic-phonetic by forensic practitioners (qualitative opinion)” reflects 

238 how the phonetic data are traditionally interpreted by many practitioners of AuPA+AcPA: 

239 though there can be quantification on the feature level (e.g., formant frequencies in Hz; values 

240 on a scale of perceived voice qualities), the results are most commonly interpreted 

241 qualitatively, e.g., as distances of the values of the unknown and known speaker that is visible 

242 in a plot of formant values, or as an experience-based judgment of how frequently a certain 

243 speaker characteristic occurs in a relevant population (Morrison et al. 2016). Such a qualitative 

244 expression of AuPA+AcPA can approach a quantitative likelihood ratio-based method if there 

245 are data available of the relevant population, for example of the f0 values of male speakers 

246 (Gold and French 2019). But for full expression of quantitative likelihood ratios, the statistical 

247 methodology has to be present, as well as all the necessary data, such as non-contemporary 

248 same-speaker and different-speaker data. 
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OSAC 2023-N-0023 Standard Guide to the Forensic 

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249 Annex 

250 

251 Bibliography 

252 

253 This is not meant to be an all-inclusive list as the group recognizes other publications on this 

254 subject may exist. At the time this document was drafted, these were some of the publications 

255 available for reference. Additionally, any mention of a particular software tool or vendor as part 

256 of this bibliography is purely incidental, and any inclusion does not imply endorsement by the 

257 authors of this document. 

258 

259 American Nurses Association. (2021). Nursing: Scope and standards of practice (4th ed.). 

260 Nursesbooks.org. 

261 

262 Cambridge University Press. (n.d.). Meanings & definitions. Cambridge Dictionary. 

263 https://dictionary.cambridge.org/

264 

265 National Commission on Forensic Science. (n.d.). Views document on definitions. 

266 https://www.justice.gov/archives/ncfs/page/file/477836/download

267 

268 National Institute of Justice. (2020). National best practices for sexual assault kits: A 

269 multidisciplinary approach. Office of Justice Programs, U.S. Department of Justice. 

270 

271 Organization of Scientific Area Committees (OSAC) for Forensic Science, Crime Scene 

272 Investigation Subcommittee. (2021). Guiding Principles for Scene Investigation and 

273 Reconstruction (OSAC 2021-N-0015). OSAC, National Institute of Standards and 

274 Technology, US Department of Commerce. 

275 https://www.nist.gov/system/files/documents/2021/09/02/OSAC%202021-

276 N0015%20Guiding%20Principles%20for%20CSI_FINAL%20OSAC%20PROPOSED%

277 20FOR%20REGISTRY.pdf

278 

279 Spellman, B. A., Eldridge, H., & Bieber, P. (2021). Challenges to reasoning in forensic science 

280 decisions. Forensic Science International. Synergy, 4, 100200. 

281 https://doiorg.mutex.gmu.edu/10.1016/j.fsisyn.2021.100200