alexa Auditory and Acoustic Features from Clue-Words Sets for
ISSN: 2157-7145

Journal of Forensic Research
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Research Article

Auditory and Acoustic Features from Clue-Words Sets for Forensic Speaker Identification and its Correlation with Probability Scales

Babita Bhall1*, Singh CP2, Rakesh Dhar3 and Rajesh Soni1

1Physics Division, Forensic Science Laboratory, Madhuban, Karnal, Haryana, India

2Physics Division, State Forensic Science Laboratory, Delhi, India

3Department of Applied Physics, Guru Jambeshwar University of Science & Technology, Hisar, India

*Corresponding Author:
Babita Bhall
Forensic Science Laboratory Physics Forensic Lab
Madhuban 132037, Karnal, Haryana, India
E-mail: [email protected]

Received date: August 10, 2016; Accepted date: September 13, 2016; Published date: September 20, 2016

Citation: Babita B, Singh CP, Rakesh D, Rajesh S (2016) Auditory and Acoustic Features from Clue-Words Sets for Forensic Speaker Identification and its Correlation with Probability Scales. J Forensic Res 7:338. doi:10.4172/2157-7145.1000338

Copyright: © 2016 Bhall B, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



An experiment carried out on speaker identification by semi-automatic measurement of parameters with the goal of collaborating numerical data as well as descriptive data with that of probability scales. The 15 sets of speech samples of 15 speakers selected randomly from 100 actual crime cases, in Hindi utterances for purpose of speaker identification test subjected to spectrographic analysis. Speaker specific acoustic parameters, namely 1st formant frequency at a particular location (F1); 2nd formant frequency at a particular location (F2); and 3rd formant frequency at a particular location (F3) measured for the set of speech samples for all the 15 speakers. Also, the auditory analysis based on the linguistic features and phonetic features noted of each of the 15 sets of speech samples. We developed software to calculate the similarity percentage for the numerical data measured on the basis of acoustic analysis and numerical values assigned to auditory parameters on the basis of auditory analysis computed according to one of the nine probability scales. Most of the existing methods take only acoustic features to obtain numerical results for the purposes of speaker identification.


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