OSAC Speaker Recognition 

Subcommittee

This document is a work product of the OSAC Speaker Recognition Subcommittee.

September 30, 2019 11:22 AM

Page 1 of 32

Process Map of Current Practices in

Forensic Speaker Recognition

Introduction

This OSAC Speaker Recognition process map arose from the need to establish a common frame of 

reference to help overcome differences in terminology and participants' background and 

experience. The development of the process map helped the participants to better understand 

current practices and communicate them in a constructive way.

Representatives of multiple U.S. government agencies, individual practitioners, and international 

experts met for three days with a facilitator to create the first draft that sketched the components 

of a forensic examination. The current version incorporates additional contributions from a variety 

of researchers and practitioners. The OSAC Speaker Recognition subcommittee would like to 

acknowledge and thank all those who participated in the development of this process map.

The process depicted does not represent the practice of any single laboratory, but generalizes the 

diverse practices of multiple laboratories. This document reflects a balance between an attempt to 

be comprehensive and the efficient use of volunteers' time. It is intended to be descriptive only, 

and its release does not imply endorsement by the OSAC Speaker Recognition Subcommittee of any 

specific approach or process. No inferences should be drawn from the inclusion or exclusion of any 

approach or process or from the level of detail provided for any particular approach or process. 

This process map is not intended to represent a best practice but rather to facilitate the 

development of future best practice documents by the OSAC Speaker Recognition Subcommittee.
OSAC Speaker Recognition Subcommittee

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Process Map of Current Practices in Forensic Speaker Recognition September 30, 2019 11:22 AM Page 2 of 32

1100

Case Suitability Inquiry Received

1200

Case 

Acceptance

2100

Preliminary 

Evaluation

2200

Content 

Review

3100

Pre-Analysis 

Observations 

and Processing

3200

Relevant 

Population 

Data

4200

Human 

Supervised 

Automatic 

Method

4300

Holistic Auditory 

Perceptual

4500

Spectrographic

4400

Expert-Driven 

Auditory Phonetic 

& Acoustic 

Phonetic Analysis

5100

Evaluation/

Generating 

Conclusion

5200

Verification

5300

Case Close-Out Terminate Case

Commentary Commentary Commentary

Commentary

Commentary

4100

Select 

analysis 

method(s)

Legend

Process start/end

Multistep subprocess

Selection from multiple options

4600

Blind Grouping
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Process Map of Current Practices in Forensic Speaker Recognition) September 30, 2019 11:22 AM Page 3 of 32

Y

To

5300

1120

All case acceptance 

criteria met?

N

 Approval received?

 Analysis timeframe appropriate?

 Technically feasible?

 Sufficient quality?

 Sufficient quantity?

 Knowns collected properly?

 Analysis requested appropriate?

 Resources available to address bias?

To 

2000

Inquiry Received

Return to Overview

Commentary

1210

Request 

Received?

1220

Assign Case 

Priority

1230

Log Chain of 

Custody

1270

Assign Case

Y

N

1285

Transfer Case to 

Assigned Examiner

1255

Obtain foil 

samples

(per agency 

policy)

1240

Will foils (imposters) 

be used in this 

examination?

Y

N

Commentary

To

5300

1250

Are there appropriate 

foils available in this 

case?

1260

Use foils from 

case

Y

N

1280

Will case be worked/

assessed by more than 

one examiner?

1290

Transfer Case to Assigned 

Examiner ensuring that no 

information about the case 

has been given to 

secondary analyst

N

Y

Legend

Single process step

Process start/end

Decision step

Multistep process that may be pre-defined in a 

standard, by lab policy, and/or by examiners

Indicates that the next or previous step is 

somewhere else on the process map
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Process Map of Current Practices in Forensic Speaker Recognition September 30, 2019 11:22 AM Page 4 of 32

2120

Inventory

(mark and describe 

evidence per agency 

policy)

2210

Best Available 

Data?

2240

Expert Consult 

Needed?

(translator, video 

analyst, etc)

2250

Consult Expert

2260

Can I continue with 

examination?

Y

N

Y

N

2130

Optimizing Playback

(finding the best way 

to playback media)

(see SWGDE Best 

Practices for Forensic 

Audio)

2220

Communicate with 

Submitter or check 

archives to obtain better 

samples

2110

Determine Physical 

Controls and Data 

Handling Requirements

(Gloves, virus checks, 

write protection, etc)

Take steps to protect the 

evidence (make a 

working copy)

2230

Can I proceed with 

current or obtained 

samples?

N

Y

N

From 

1000

To 

3000

Y

Return to Overview

To

5300

Commentary

Commentary

Legend

Single process step

Decision step

Multistep process that may be pre-defined in a 

standard, by lab policy, and/or by examiners

Indicates that the next or previous step is 

somewhere else on the process map
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From 

2000

To 

4000

3120

Do speaking style 

and recording 

conditions match 

between Q

and K?

3125

Document the 

matching and 

mismatching 

conditions and 

features of the Q 

and K, as applicable

3130

Do I have a 

method that works 

with these data?

3135

Will processing 

permit a method 

selection?

3145

Select appropriate 

method(s)

3150

Will processing 

improve 

performance of 

method?

3165

Document newly 

observed properties

3160

Did processing 

elucidate any new 

intrinsic or extrinsic 

properties?

3210

Do I have

appropriate data to 

carry out downstream 

processing?

3220

Can I obtain or 

simulate comparable 

test data?

3240

Obtain or 

simulate 

required data

3250

Continue to method

(consider contextual bias 

when deciding which 

method to use first)

3230

Can I continue with 

examination?

Y

N N

Y Y Y

N

N

Y

Y

Y

3155

Process Data

(see list of 

processing 

techniques)

3115

Document intrinsic 

and extrinsic 

properties in Q and K

3110

Observe intrinsic and 

extrinsic properties in 

Q and K

(Q is the limiting 

evidence)

N

3105

Select question of 

interest for analysis

Y

3140

Process 

Evidence 

N

N

N

Return to Overview

To

5300

To

5300

Commentary

Commentary

From 

5100

Legend

Process step that results in documentation

Single process step

Decision step

Multistep process that may be pre-defined in a 

standard, by lab policy, and/or by examiners

Indicates that the next or previous step is 

somewhere else on the process map
OSAC Speaker Recognition Subcommittee

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Process Map of Current Practices in Forensic Speaker Recognition September 30, 2019 11:22 AM Page 6 of 32

From 

3000

Y

Return to Overview

4115

Is this method 

appropriate?

4110

Assess suitability of 

Human Supervised 

Automatic Analysis

4120

Assess suitability of 

Holistic Auditory 

Perceptual Analysis

4130

Assess suitability of 

Expert-Driven Auditory 

Phonetic and Acoustic 

Phonetic Analysis

4140

Assess suitability of 

Spectrographic Analysis

4125

Is this method 

appropriate?

4135

Is this method 

appropriate?

4145

Is this method 

appropriate?

To 

4300

Y

To 

4400

Y

To 

4500

Y

Commentary

To 

4100

N

To 

4100

N

To 

4100

N

To 

4100

N

4150

Assess suitability of Blind 

Grouping Method

4155

Is this method 

appropriate?

To 

4600

Y

To 

4100

N

To 

4200

Legend

Single process step

Decision step

Indicates that the next or previous step 

is somewhere else on the process map

Acoustic Phonetic Statistical Analysis (Semiautomatic Analysis)

Acoustic Phonetic Statistical Analysis (or Semiautomatic Analysis) 

is similar to Human Supervised Automatic Analysis (4200), but 

uses features derived via phonetic analysis, including humansupervised measurements of acoustic properties of the speech 

recording.
OSAC Speaker Recognition Subcommittee

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From 

4100

To 

5000

Return to Overview

4205

Is diarization 

needed?

4215

Optimize the 

selection

4220

Create new file 

containing one 

speaker per 

recording

4223

Have I separated 

all speakers of 

interest?

4225

Can I continue with 

examination?

(data sufficiency)

4230

Conduct expert 

critical listening

(set expectations 

and parameter 

selection)

4235

Do I have a system 

optimized for my 

case data?

4240

Can I optimize my 

automated speaker 

recognition system 

for the case 

conditions?

4237

Select ASR

(based on classifier/

algorithm, 

hyperparameter/

model, reference 

population, settings)

4280

Report only

numeric result (e.g. 

score, likelihood

ratio)?

4282

Convert result 

to verbal 

description

4290

Complete Human 

Supervised Automatic 

Method? (formerly 

Run data on another 

ASR?)

4285

Report results

Y Y

Y

N

Y

N

Y Y

4210

Diarize speakers 

in recording

Y

N

N

Y

N

Commentary

4000

4241

Train system 

using training 

data

4242

Test system 

using 

development 

data

4243

Performance 

adequate?

4244

Make changes 

to system

N

4238

Train system using 

training data

4246

Test system 

using validation 

data

4247

Report 

validation 

results

4249

Apply system to 

known-speaker and 

questioned-speaker 

recordings

4248

Performance 

adequate?

Y

N

4275

Report that system 

performance under the case 

conditions is inadequate to 

proceed with an evaluation of 

the strength of evidence 

associated with the knownand questioned-speaker 

recordings

N

4255

Interpret ASR output

(refer to distributions 

on comparable 

systems and data and 

associated error rates)

4257

Assess 

confidence in 

ASR results

4260

Is the output

logically consistent 

with regards to type 

of mismatch?

4265

Stop analysis?

Y

N

N

4270

Adjust model/

reference 

population and/or 

settings.

4245

Perform validation 

test?

Y

N

4250

Post-process 

results?

N

Y

Y

4267

Document issues noted 

in box 4260

N
OSAC Speaker Recognition Subcommittee

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Return to Overview

4310

Is diarization 

needed?

4330

Optimize the 

selection

4340

Create new file 

containing one speaker 

per recording

4350

Have I segmented 

all speakers of 

interest?

4360

Can I continue

with examination?

(sufficient data)

4370

Conduct expert 

critical listening

(set expectations 

and parameter 

selection)

4380

Document results

Y

Y

4320

Diarize speakers 

in recording

Y

N

N

N

To 

5000

From 

4100

Commentary

4000

Legend

Process step that results in documentation

Single process step

Decision step

Multistep process that may be pre-defined in 

a standard, by lab policy, and/or by examiners

Indicates that the next or previous step is 

somewhere else on the process map
OSAC Speaker Recognition Subcommittee

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Return to Overview

4442

Obtain 

orthographic 

transcript

4447

Document 

measurements of 

features selected

(quantity/quality/

practicality)

4460

Document 

typicality

4475

Document 

similarity

4465

Use automation 

to evaluate 

similarity?

4467

Select 

appropriate tool 

to evaluate 

similarity

4410

Is diarization 

needed?

4415

Optimize the 

selection

4420

Create new file 

containing one 

speaker per 

recording

4425

Have I segmented 

all speakers of 

interest?

4430

Can I continue with 

examination?

(sufficient data)

Y Y

N

Y

4490

Document results

4435

Conduct expert 

critical listening

(set expectations 

and parameter 

selection)

4440

Need 

orthographic 

transcript?

4405

Do I have the 

language, phonetic, 

sociolinguistic, dialect 

expertise appropriate

for data?

Y Y

4480

Is the output 

logically 

consistent ?

Y

4485

Is analysis 

complete? 

(formerly Rerun/

select another 

tool?)

4412

Diarize speakers 

in recording

4445

Classify/Extract 

auditory/acoustic 

features of interest

Y

Y

N

N

N N

4450

Use automation 

to evaluate

typicality?

4452

Select 

appropriate tool 

to evaluate 

typicality 

N

Y

N

N N

4455

Evaluate 

typicality

4470

Evaluate 

similarity

From 

4100

To 

5000

Commentary

4000

Legend

Process step that results in documentation

Single process step

Decision step

Multistep process that may be pre-defined in a 

standard, by lab policy, and/or by examiners

Indicates that the next or previous step is 

somewhere else on the process map
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4510

Conduct examination 

according to standard

(e.g. IAI Standard, 

reference JFI (1991) 41:5)

4520

Document results From 

4100

To 

5000

Return to Overview

Commentary

4000

Legend

Multistep process that may be pre-defined in a 

standard, by lab policy, and/or by examiners

Single process step

Indicates that the next or previous step is 

somewhere else on the process map
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Return to Overview 4000

Legend

Process step that results in documentation

Single process step

Decision step

Multistep process that may be pre-defined in 

a standard, by lab policy, and/or by examiners

Indicates that the next or previous step is 

somewhere else on the process map

4615

Is diarization 

needed?

4630

Create new file per speaker 

per recording. Strip the 

recording from any speech 

that contains name, 

addresses, etc. that would 

help linking fragments.

4605

Are there appropriate 

foils available in the 

case?

Y

4620

Diarize speakers 

in recordings

N

From 

4100

4610

Create a list of speakers to 

be included in the blind 

grouping, containing at 

least each Q, and K and 0 

or more foils.

Y

N

4635

Is it possible to make 1 or 

more fragments of max 20 

seconds for each speaker 

and each recording?

N

Y

4640

Is it possible to make

true same speaker and true 

different speaker

pairs?

4645

Create a list of fragments 

per speaker per recording, 

max 20 fragments.

Y

N

4650

Create the max 20 fragments into 

separate audio files, all with the 

same audio specs (take the one 

with the highest quality).

4655

Is there enough speaker 

variation in each of the 

fragments (so as not to bias 

by selection)?

4660

Anonymize and collect all the 

fragments in one larger audio file 

in random order (but make a 

lookup table to retrieve the 

origins). Present the larger file to 

researcher #2.

4665

Document composition 

of larger audio file.

Y

N

4625

Optimize the 

selection

Commentary

To 

5000

4670

Compare each of the fragments 

with each other and group them 

by perceived speaker identity.

4675

Is each of the fragments 

suitable for grouping?

4680

Separate out the unsuitable 

fragments.

4685

Document a grouping, 

including perceived 

similarity within groups 

and perceived dissimilarity 

between groups and the 

ungrouped fragments.

N

Y

4695

Document results

4690

Interpret grouping using 

lookup table and including 

correctness of grouping in 

ground truth.
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From 

4000

5310

Notify requestor and 

transmit report, 

where appropriate

Terminate Review

Abort

5250

Consensus 

conclusion 

reached?

5260

Resolve conflicts

(Follow agency 

conflict 

resolution policy )

5205

Verify conclusions?

(according to agency 

policy)

5210

Conduct blind 

review?

5220

Conduct fully 

blinded?

N

5240

Conduct 

independent 

analysis

(examiner 

begins at 310)

5230

Conduct 

technical 

review (per 

agency policy)

5290

Conduct 

administrative 

review

Y Y Y

N

N

Y

5270

Does the report 

need to be 

changed?

5225

Share limited case 

data (per agency 

policy)

N

5280

Revise Report 

based on 

consensus review

Y

N

5105

Multiple 

methods 

used?

5115

Combine (fuse) 

results?

5165

Document Opinion 

or Conclusions

Y

5120

Assign weight 

and combine 

(fuse) results per 

agency policy

N

5180

Draft preliminary 

report

N

5110

Assess 

confidence for 

each method 

used

5125

Formulate 

conclusion

5150

Will 

consultation be 

helpful?

5155

Share case 

detail

Seek input 

5160

Will additional 

analysis be 

helpful?

5170

Are all case 

questions 

answered?

N

Y Y To 

4000

Y

N

N

Y

Return to Overview

Commentary

Commentary

Commentary

To 

3000

Legend

Process step that results in documentation

Single process step

Decision step

Multistep process that may be pre-defined in 

a standard, by lab policy, and/or by examiners

Indicates that the next or previous step is 

somewhere else on the process map
OSAC Speaker Recognition 

Subcommittee

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September 30, 2019 11:22 AM

Page 13 of 32

Process Map of Current Practices in

Forensic Speaker Recognition

Return to Overview 1000

Process Step

1000 – Administrative Assessment

Description

Terms and Definitions

Comments

Issues

References

Revised

September 30, 2019 11:22 AM
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Subcommittee

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September 30, 2019 11:22 AM

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Process Map of Current Practices in

Forensic Speaker Recognition

Return to Overview 1000

Process Step

1100 – Case Suitability

Description

Terms and Definitions

Comments

Issues

References

Revised

September 30, 2019 11:22 AM

1100
OSAC Speaker Recognition 

Subcommittee

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September 30, 2019 11:22 AM

Page 15 of 32

Process Map of Current Practices in

Forensic Speaker Recognition

Return to Overview 1000

Process Step

1200 – Case Acceptance

Description

Terms and Definitions

Comments

Issues

References

Revised

September 30, 2019 11:22 AM

1200
OSAC Speaker Recognition 

Subcommittee

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Process Map of Current Practices in

Forensic Speaker Recognition

Return to Overview 2000

Process Step

2000 – Technical Assessment

Description

Terms and Definitions

Comments

Issues

References

Revised

September 30, 2019 11:22 AM
OSAC Speaker Recognition 

Subcommittee

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Process Map of Current Practices in

Forensic Speaker Recognition

Return to Overview 2000

Process Step

2100 – Preliminary Evaluation

Description

Terms and Definitions

Comments

Issues

References

SWGDE Best Practices for Forensic Audio.

Revised

September 30, 2019 11:22 AM

2100
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Subcommittee

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Page 18 of 32

Process Map of Current Practices in

Forensic Speaker Recognition

Return to Overview 2000

Process Step

2200 – Content Review

Description

Terms and Definitions

Comments

Issues

References

Revised

September 30, 2019 11:22 AM

2200
OSAC Speaker Recognition 

Subcommittee

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Process Map of Current Practices in

Forensic Speaker Recognition

Return to Overview 3000

Process Step

3000 – Processing

Description

Terms and Definitions

Comments

Issues

References

Revised

September 30, 2019 11:22 AM
OSAC Speaker Recognition 

Subcommittee

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Page 20 of 32

Process Map of Current Practices in

Forensic Speaker Recognition

Return to Overview 3000

Process Step

3100 – Pre-Analysis Observations and Processing

Description

Terms and Definitions

3155: Types of processing

 Enhancement for intelligibility or listenability (e.g. tone removal, spectral shaping, adaptive filtering, etc.)

 Normalization

 Convert sampling rate / bit depth

 Channel conversion

 DC offset

 Anti-aliasing

Comments

Issues

References

Revised

September 30, 2019 11:22 AM

3100
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Subcommittee

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Process Map of Current Practices in

Forensic Speaker Recognition

Return to Overview 3000

Process Step

3200 – Relevant Population Data

Description

This block describes the necessary use of different data sets for testing system performance . Evaluation typically requires training data and 

test data sets, but other data may be required (e.g. for calibration).

Term ands Definitions

Comments

Issues

References

Morrison, G.S., Thompson, W.C. (2017). Assessing the admissibility of a new generation of forensic voice comparison testimony. Columbia 

Science and Technology Law Review, 18, 326–434 §3.1. http://www.stlr.org/cite.cgi?volume=18&article=morrisonThompson 

(Preprints: https://ssrn.com/abstract=2883767 https://www.newton.ac.uk/files/preprints/ni16053.pdf)

Morrison, G.S., Enzinger, E., Zhang, C. (2018). Forensic speech science. In I. Freckelton, H. Selby (Eds.), Expert Evidence, §99.140ff. Sydney, 

Australia: Thomson Reuters. (Preprint: http://expert-evidence.forensic-voice-comparison.net/)

Morrison, G.S., Enzinger, E., Zhang, C. (2016). Refining the relevant population in forensic voice comparison – A response to Hicks et alii 

(2015) The importance of distinguishing information from evidence/observations when formulating propositions. Science & Justice, 

56, 492–497. http://dx.doi.org/10.1016/j.scijus.2016.07.002 [see also: http://geoff-morrison.net/#replies_to_Hicks_et_al_2015]

Robertson B., Vignaux G.A., Berger C.E.H. (2016). Interpreting Evidence: Evaluating Forensic Science in the Courtroom, 2nd Ed., Chichester 

(UK): Wiley. http://dx.doi.org/10.1002/9781118492475

Revised

September 30, 2019 11:22 AM

3200
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Subcommittee

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Process Map of Current Practices in

Forensic Speaker Recognition

Return to Overview 4000

Process Step

4000 – Analysis

Description

Terms and Definitions

Comments

Issues

References

Morrison, G.S., Thompson, W.C. (2017). Assessing the admissibility of a new generation of forensic voice comparison testimony. Columbia 

Science and Technology Law Review, 18, 326–434 §2.3. http://www.stlr.org/cite.cgi?volume=18&article=morrisonThompson 

(Preprints: https://ssrn.com/abstract=2883767 https://www.newton.ac.uk/files/preprints/ni16053.pdf)

Morrison, G.S., Enzinger, E., Zhang, C. (2018). Forensic speech science. In I. Freckelton, H. Selby (Eds.), Expert Evidence, §99.650ff. Sydney, 

Australia: Thomson Reuters. (Preprint: http://expert-evidence.forensic-voice-comparison.net/)

Morrison, G.S., Sahito, F.H., Jardine, G., Djokic, D., Clavet, S., Berghs, S., and Goemans Dorny, C. 2017. INTERPOL survey of the use of speaker 

identification by law enforcement agencies. Forensic Science International, 263: 92–100. http://dx.doi.org/10.1016/

j.forsciint.2016.03.044

Morrison, G.S. (2018). Admissibility of forensic voice comparison testimony in England and Wales. Criminal Law Review, (1), 20–33. 

(Available: http://geoff-morrison.net/#Admissibility_EW_2018)

Morrison, G.S. (2014). Distinguishing between forensic science and forensic pseudoscience: Testing of validity and reliability, and approaches 

to forensic voice comparison. Science & Justice, 54, 245–256. http://dx.doi.org/10.1016/j.scijus.2013.07.004

Revised

September 30, 2019 11:22 AM
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Subcommittee

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Process Map of Current Practices in

Forensic Speaker Recognition

Return to Overview 4000

Process Step

4100 – Select Analysis Method(s)

Description

Terms and Definitions

Comments

Issues

References

For Acoustic Phonetic Statistical Analysis (Semiautomatic Analysis):

Rose P. (2002). Forensic speaker identification. London: Taylor and Francis.

Rose P. (2013). Where the science ends and the law begins - likelihood ratio-based forensic voice comparison in a $150 million telephone 

fraud. International Journal of Speech, Language and the Law, pp. 227-324. http://dx.doi.org/10.1558/ijsll.v20i2.277

Rose P. (2017). Likelihood ratio-based forensic voice comparison with higher level features research and reality . Computer Speech & 

Language, 45, pp. 475-502. http://dx.doi.org/10.1016/j.csl.2017.03.003

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4100
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Subcommittee

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Process Map of Current Practices in

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Process Step

4200 – Human Supervised Automatic Analysis

Description

This block describes the necessary use of different data sets for testing system performance . Evaluation typically requires training data and 

test data sets, but other data may be required (e.g. for calibration).

Terms and Definitions

4230: “Expert critical listening” is defined as ...

Comments

Issues

References

Morrison, G.S., Thompson, W.C. (2017). Assessing the admissibility of a new generation of forensic voice comparison testimony. Columbia 

Science and Technology Law Review, 18, 326–434 §2.3.4. http://www.stlr.org/cite.cgi?volume=18&article=morrisonThompson 

(Preprints: https://ssrn.com/abstract=2883767 https://www.newton.ac.uk/files/preprints/ni16053.pdf)

Morrison, G.S., Enzinger, E., Zhang, C. (2018). Forensic speech science. In I. Freckelton, H. Selby (Eds.), Expert Evidence, §99.720ff. Sydney, 

Australia: Thomson Reuters. (Preprint: http://expert-evidence.forensic-voice-comparison.net/)

Ramos Castro D. (2007). Forensic evaluation of the evidence using automatic speaker recognition systems. Doctoral dissertation, 

Autonomous University of Madrid.

Becker T. (2012). Automatischer forensischer Stimmenvergleich [Automatic forensic voice comparison]. Doctoral dissertation, University of 

Trier.

Enzinger E. (2016). Implementation of forensic voice comparison within the new paradigm for the evaluation of forensic evidence . Doctoral 

dissertation, University of New South Wales. http://handle.unsw.edu.au/1959.4/55772

Morrison G.S., Enzinger E. (2016). Multi-laboratory evaluation of forensic voice comparison systems under conditions reflecting those of a 

real forensic case (forensic_eval_01) – Introduction, Speech Communication, 85, pp. 119–126. http://dx.doi.org/10.1016/

j.specom.2016.07.006

Marks D.B. (2017). A framework for performing forensic and investigatory speaker comparisons using automated methods. Master’s thesis, 

University of Colorado Denver.

Hansen J.H.L., Hasan T. (2015). Speaker recognition by machines and humans: A tutorial review, IEEE Signal Processing Magazine, 

November, pp. 74–99 http://dx.doi.org/10.1109/MSP.2015.2462851

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Process Map of Current Practices in

Forensic Speaker Recognition

Return to Overview 4000

Process Step

4300 – Holistic Auditory Perceptual Analysis

Description

Terms and Definitions

Comments

Issues

References

Revised

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4300
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Process Map of Current Practices in

Forensic Speaker Recognition

Return to Overview 4000

Process Step

4400 – Expert Driven Auditory Phonetic and Acoustic Phonetic Analysis

Description

Terms and Definitions

Comments

Issues

References

Morrison, G.S., Thompson, W.C. (2017). Assessing the admissibility of a new generation of forensic voice comparison testimony. Columbia 

Science and Technology Law Review, 18, 326–434 §2.3.1 §2.3.3. http://www.stlr.org/cite.cgi?volume=18&article=morrisonThompson 

(Preprints: https://ssrn.com/abstract=2883767 https://www.newton.ac.uk/files/preprints/ni16053.pdf)

Morrison, G.S., Enzinger, E., Zhang, C. (2018). Forensic speech science. In I. Freckelton, H. Selby (Eds.), Expert Evidence, §99.660ff, §99.700ff. 

Sydney, Australia: Thomson Reuters. (Preprint: http://expert-evidence.forensic-voice-comparison.net/)

Hollien H. (2002). Forensic voice identification. San Diego: Academic.

Hollien H. (2016). An approach to speaker identification. Journal of Forensic Sciences, 61, pp. 334–344. http://dx.doi.org/10.1111/1556-

4029.13034

Hollien H., Didla G., Harnsberger J.D., Hollien K.A. (2016). The case for aural perceptual speaker identification. Forensic Science International, 

269, pp. 5–20. http://dx.doi.org/10.1016/j.forsciint.2016.08.007

Jessen M. (2008). Forensic phonetics. Language and Linguistics Compass, 2, pp. 671–711. http://dx.doi.org/10.1111/j.1749-

818x.2008.00066.x

Jessen M. (2012). Phonetische und Linguistische Prinzipien des Forensischen Stimmenvergleichs [Phonetic and linguistic principles of forensic 

voice comparison]. Munich, Germany: Lincom.

Nolan F. (1997). Speaker recognition and forensic phonetics. In: Hardcastle W.J., Laver J., The handbook of phonetic sciences. Oxford: 

Blackwell.

Nolan F. (2005). Forensic speaker identification and the phonetic description of voice quality. In: Hardcastle, W.J., Beck J.M. (Eds.), A figure 

of speech: A festschrift for John Laver (pp. 385–411). Mahwah, NJ: Erlbaum.

French J.P., Nolan, F., Foulkes, P., Harrison P., McDougall, K. (2010). The UK position statement on forensic speaker comparison: A rejoinder 

to Rose and Morrison. International Journal of Speech, Language and the Law, 17, pp. 143–152. http://dx.doi.org/10.1558/

ijsll.v17i1.143

Zhang C. (2009). 法庭语音技术研究 [Forensic Speech Technology Research]. 中国社会出版社 [China Social Press].

Rose P. (2002). Forensic speaker identification. London: Taylor and Francis.

Rose P. (2013). Where the science ends and the law begins- likelihood ratio-based forensic voice comparison in a $150 million telephone 

fraud. International Journal of Speech, Language and the Law, pp. 227–324. http://dx.doi.org/10.1558/ijsll.v20i2.277

Rose P. (2017). Likelihood ratio-based forensic voice comparison with higher level features: Research and reality. Computer Speech & 

Language, 45, pp. 475–502. http://dx.doi.org/10.1016/j.csl.2017.03.003

Lindh J. (2017). Forensic comparison of voices, speech and speakers tools and methods in forensic phonetics. PhD dissertation. University of 

Gothenburg.

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Process Map of Current Practices in

Forensic Speaker Recognition

Return to Overview 4000

Process Step

4500 – Spectrographic / Aural Spectrographic Analysis

Description

Terms and Definitions

Comments

Issues

References

IAI Standard, reference JFI (1991) 41:5

Morrison, G.S., Thompson, W.C. (2017). Assessing the admissibility of a new generation of forensic voice comparison testimony. Columbia 

Science and Technology Law Review, 18, 326–434 §2.3.2. http://www.stlr.org/cite.cgi?volume=18&article=morrisonThompson 

(Preprints: https://ssrn.com/abstract=2883767 https://www.newton.ac.uk/files/preprints/ni16053.pdf)

Morrison, G.S., Enzinger, E., Zhang, C. (2018). Forensic speech science. In I. Freckelton, H. Selby (Eds.), Expert Evidence, §99.680ff. Sydney, 

Australia: Thomson Reuters. (Preprint: http://expert-evidence.forensic-voice-comparison.net/)

Kersta L.G. (1962). Voiceprint identification. Nature, 196, pp. 1253–1257. http://dx.doi.org/10.1038/1961253a0

Tosi O. (1979). Voice Identification: Theory and Legal Applications. Baltimore, MD: University Park Press.

National Research Council (1979). On the theory and practice of voice identification. Washington: National Academies Press.

Cáo Hónglín 曹洪林, Lǐ Jìngyáng 李敬陽, Wáng Yīnglì 王英利, Kǒng Jiāngpíng孔江平 (2013). Lùn shēngwén jiàndìng yìjiàn de biǎoshù xíngshì 

論聲紋鑒定意見的表述形式 [On Expert Opinion of Forensic Speaker Identification], Zhèngjù Kēxué 證據科學 [Evidence Science], 21, 

605–624

American Board of Recorded Evidence (1999). Voice comparison standards. Available at: http://www.tapeexpert.com/pdf/abrevoiceid.pdf

Poza F., Begault D.R. (2005). Voice identification and elimination using aural-spectrographic protocols. In: Proceedings of the Audio 

Engineering Society 26th International Conference: Audio Forensics in the Digital Age. Paper No. 1-1.

Gruber J.S., Poza F. (1995). Voicegram Identification Evidence. In: American Jurisprudence Trials. Westlaw. Vol. 54.

Solan L.M., Tiersma P.M. (2003). Hearing voices: Speaker identification in court. Hastings Law Journal, 54, pp. 373–435.

Meuwly D. (2003a). Le mythe de l’empreinte vocale I. Revue Internationale de Criminologie et Police Technique, 56, pp. 219–236.

Meuwly D. (2003b). Le mythe de l’empreinte vocale II. Revue Internationale de Criminologie et Police Technique, 56, pp. 361–374.

Lindh J. (2017). Forensic comparison of voices, speech and speakers tools and methods in forensic phonetics. PhD dissertation. University of 

Gothenburg.

Revised

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4500
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Page 28 of 32

Process Map of Current Practices in

Forensic Speaker Recognition

Return to Overview 4000

Process Step

4600 – Blind Grouping Method

Description

Terms and Definitions

Comments

Issues

References

Cambier-Langeveld T., van Rossum M. and Vermeulen J. 2014. Whose voice is that? Challenges in forensic phonetics. In van Heuven V. and 

Caspers J., editors, Above and Beyond the Segments: Experimental Linguistics and Phonetics, pages 14–27. John Benjamins, 

Amsterdam.

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Process Map of Current Practices in

Forensic Speaker Recognition

Return to Overview 5000

Process Step

5000 – Results

Description

Terms and Definitions

Comments

Issues

References

Revised

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Subcommittee

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Process Map of Current Practices in

Forensic Speaker Recognition

Return to Overview 5000

Process Step

5100 – Evaluation / Generating Conclusion

Description

References

Morrison, G.S., Thompson, W.C. (2017). Assessing the admissibility of a new generation of forensic voice comparison testimony. Columbia Science and 

Technology Law Review, 18, 326–434 §3. http://www.stlr.org/cite.cgi?volume=18&article=morrisonThompson (Preprints: https://ssrn.com/

abstract=2883767 https://www.newton.ac.uk/files/preprints/ni16053.pdf)

Morrison, G.S., Enzinger, E., Zhang, C. (2018). Forensic speech science. In I. Freckelton, H. Selby (Eds.), Expert Evidence, §99.140ff. Sydney, Australia: 

Thomson Reuters. (Preprint: http://expert-evidence.forensic-voice-comparison.net/)

Morrison, G.S., Sahito, F.H., Jardine, G., Djokic, D., Clavet, S., Berghs, S., and Goemans Dorny, C. 2017. INTERPOL survey of the use of speaker identification 

by law enforcement agencies. Forensic Science International, 263: 92–100. http://dx.doi.org/10.1016/j.forsciint.2016.03.044

Robertson B., Vignaux G.A., Berger C.E.H. (2016). Interpreting Evidence: Evaluating Forensic Science in the Courtroom, 2nd Ed., Chichester (UK): Wiley. 

http://dx.doi.org/10.1002/9781118492475

Kaye D.H., Bernstein D.A., Mnookin J.L. (2011). Quantitative testimony on forensic science identification. In: The New Wigmore: A Treatise on Evidence: 

Expert Evidence, 2nd ed., New York: Wolters Kluwer, Ch. 14.

Jackson, G. 2009. Understanding forensic science opinions. In Fraser, J. and Williams, R., editors, Handbook of Forensic Science, pages 419–445. Willan, 

Cullompton, UK.

Balding, D.J. and Steele, C. 2015. Weight-of-evidence for forensic DNA profiles (2nd edition). Wiley, Chichester, UK. https://doi.org/10.1002/9781118814512

Hicks, T., Buckleton, J.S., Bright, J.A. and Taylor D. 2015. A Framework for interpreting evidence. In Buckleton, J.S., Bright, J.A. and Taylor, D., editors, 

Forensic DNA Evidence Interpretation (2nd edition), pages 37–86. CRC, Boca Raton. 

Aitken, C.G.G., Berger, C.E.H., Buckleton, J.S., Champod, C., Curran. J.M., Dawid, A.P., Evett, I.W., Gill, P., González-Rodríguez, J., Jackson, G., Kloosterman, 

A., Lovelock, T., Lucy, D., Margot, P., McKenna, L., Meuwly, D., Neumann, C., Nic Daéid, N., Nordgaard, A., Puch-Solis, R., Rasmusson, B., Redmayne, 

M., Roberts, P., Robertson, B., Roux, C., Sjerps, M.J., Taroni, F., Tjin-A-Tsoi, T., Vignaux, G.A., Willis, S.M. and Zadora, G. 2011. Expressing evaluative 

opinions: A position statement. Science & Justice, 51: 1–2. http://dx.doi.org/10.1016/j.scijus.2011.01.002

Willis, S.M., McKenna, L., McDermott, S., O’Donell, G., Barrett, A., Rasmusson, A., Nordgaard, A., Berger, C.E.H., Sjerps, M.J., Lucena-Molina, J.J., Zadora, G., 

Aitken, C.G.G., Lunt, L., Champod, C., Biedermann, A., Hicks, T.N., Taroni, F. 2015. ENFSI guideline for evaluative reporting in forensic science. 

European Network of Forensic Science Institutes. http://enfsi.eu/wp-content/uploads/2016/09/m1_guideline.pdf (last visited January 2018).

President’s Council of Advisors on Science and Technology 2016. Forensic Science in Criminal Courts: Ensuring Scientific Validity of Feature-Comparison 

Methods. https://obamawhitehouse.archives.gov/administration/eop/ostp/pcast/docsreports/ (last visited 6 February 2017).

Morrison, G.S., Kaye, D.H., Balding, D.J., Taylor, D., Dawid, P., Aitken, C.G.G., Gittelson, S., Zadora, G., Robertson, B., Willis, S.M., Pope, S., Neil, M., Martire, 

K.A., Hepler, A., Gill, R.D., Jamieson, A., de Zoete, J., Ostrum, R.B., Caliebe, A. (2016/2017). A comment on the PCAST report: Skip the “match”/“nonmatch” stage. Forensic Science International, 272, e7–e9. http://dx.doi.org/10.1016/j.forsciint.2016.10.018 (Preprint at: http://forensicevaluation.net/PCAST2016/)

National Commission on Forensic Science (2016). Recommendations on use of the term “reasonable scientific certainty”. https://www.justice.gov/ncfs/file/

839726/download

American Board of Recorded Evidence (1999). Voice comparison standards. Available at: http://www.tapeexpert.com/pdf/abrevoiceid.pdf

French J.P. and Harrison P. 2007. Position statement concerning use of impressionistic likelihood terms in forensic speaker comparison cases. International 

Journal of Speech, Language and the Law, 14: 137–144. https://doi.org/10.1558/ijsll.v14i1.137

Rose, P. and Morrison G.S. 2009. A response to the UK position statement on forensic speaker comparison. International Journal of Speech, Language and 

the Law, 16: 139–163. http://dx.doi.org/10.1558/ijsll.v16i1.139

French J.P., Nolan, F., Foulkes, P., Harrison P., McDougall, K. (2010). The UK position statement on forensic speaker comparison: A rejoinder to Rose and 

Morrison. International Journal of Speech, Language and the Law, 17, pp. 143–152. http://dx.doi.org/10.1558/ijsll.v17i1.143

Morrison, G.S. (2010). Forensic voice comparison. In I. Freckelton, H. Selby (Eds.), Expert Evidence (Ch. 99). Sydney, Australia: Thomson Reuters.

Morrison, G.S. (2014). Distinguishing between forensic science and forensic pseudoscience: Testing of validity and reliability, and approaches to forensic 

voice comparison. Science & Justice, 54, 245–256. http://dx.doi.org/10.1016/j.scijus.2013.07.004

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5100

Terms and Definitions

Comments
OSAC Speaker Recognition 

Subcommittee

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Process Map of Current Practices in

Forensic Speaker Recognition

Return to Overview 5000

Process Step

5200 – Verification

Description

Terms and Definitions

Comments

Issues

References

Revised

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Process Map of Current Practices in

Forensic Speaker Recognition

Return to Overview 5000

Process Step

5300 – Case Close-Out

Description

Terms and Definitions

Comments

Issues

References

Revised

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5300