Version 1 / Date of Issue: 10.29.15 / Issuing Authority: Forensic Science Standards Board (FSSB)

OSAC Research Needs Assessment Form

Title of research need:

Keywords:

Submitting subcommittee(s): Date Approved:

(If SAC review identifies additional 

subcommittees, add them to the box above.)

Background information:

1. Description of research need:

2. Key bibliographic references relating to this research need:

3a. In what ways would the research results improve current laboratory capabilities?

Automatic speaker recognition (ASR) is developed using available data to improve the system 

performance. As ASR matures researchers have realized that the data available for development is 

not representative of the data received by case workers. In this document we propose that multiple 

datasets are collected that more closely represent the data that examiners are faced with and that 

technology is evaluated on 

Evaluating the existing ASR methods using data more representative of what the labs receive for 

analysis will (1) help labs better understand the capabilities and limitations of the ASR tools they use 

and (2) help identify capability gaps that research can be focused on. The requirement for multiple 

datasets arises out of the wide variation in the types of data received by the various users of ASR in 

the United States.

Forensic speaker recognition evaluation

Speaker Recognition 2/1/16
Version 1 / Date of Issue: 10.29.15 / Issuing Authority: Forensic Science Standards Board (FSSB)

3b. In what ways would the research results improve understanding of the scientific basis for the 

subcommittee(s)?

3c. In what ways would the research results improve services to the criminal justice system?

4. Status assessment (I, II, III, or IV): 

This research need has been identified by one or more subcommittees of OSAC and is being provided as an 

informational resource to the community.

The RDT&E Task Group understands that there is a critical need to compile a set of corpora that supports 

the evaluation of systems on data that closely resembles the conditions seen by forensic speech examiners. 

As such, we support the initiative proposed by Dr. Reva Schwartz to fund a data collection that produces 

data that is realistic and is able to support research and evaluation of automatic systems for the foreseeable 

future.

Major gap 

in current 

knowledge

Minor gap 

in current 

knowledge

No or limited

current research 

is being 

conducted

I III

Existing current 

research is being 

conducted

II IV

The more realistic testing of the existing tools will lead to additional detailed studies of the 

phenomena causing the existing ASR methods to produce erroneous results. The results of those 

studies can be expected to improve not only our understanding of ASR, but also of other areas of 

speech processing and forensics.

A better understanding of our current ASR capabilities will help ensure that investigators, judges and 

juries are getting the best possible information when ASR is involved. The expected improvement of 

those capabilities by developments based on those research results will also allow the more effective 

use of ASR by the justice system and other USG users.

 I
Version 1 / Date of Issue: 10.29.15 / Issuing Authority: Forensic Science Standards Board (FSSB)

// Approval date:

(Approval is by majority vote of subcommittee. Once approved, forward to SAC.)

1. Does the SAC agree with the research need? Yes ⃝ No ⃝ 

2. Does the SAC agree with the status assessment? Yes ⃝ No ⃝

If no, what is the status assessment of the SAC:

Approval date:

(Approval is by majority vote of SAC. Once approved, forward to NIST for posting.)

2/1/16 Subcommittee

SAC

6/15/16