Forensic investigation for twin identification from speech: perceptual and gamma-tone features and models

To assist an investigation process, forensic experts compare and analyze audio recordings. Speech utterances are compared by humans and/or machines for use in court for investigation. Scientific research community insists for specific automatic or human-based approach to identify uniquxy2e audio features from identical twins group. Filters can be employed to enhance an audio recording for improving clarity. This may entail removal of unnecessary noise to enrich the intelligibility of speech. Forensic audio experts can examine a variety of characteristics of the audio recording to decide the possibility of alterations in the collected evidences. This includes confirming the integrity and authenticating that the content is what it purports to be. Thiswork named as FIST ( F orensic I nvestigation for Twin Identification from S peech: Perceptual and Gamma- t one Features and Models) proposes an automated system to identify a twin from identical twin pairs by the use of gamma-tone features and perceptual features.The proposed features are excerpted from the set of training speeches and templates are created for each twin based on vector quantisation (VQ), Fuzzy C means clustering (FCM) and multivariate hidden Markov modelling (MHMM) techniques. For testing, features are extracted from the set of test utterances and worked out to the templates for classification. Based on the type of classifier used, classification of twin is carried out with minimum distance and maximum loglikelihood value. The proposed features are examinedfor sub-optimal and true success rates as key performance metrics to assess the system and also a comparative analysis is made across the proposed features. Among the inspected features, Gammatone energy features expose better performance in comparison to perceptual features by attaining the overall sub-optimal success rate and true success rate as97.8375% and 92.75% for Gammatone energy features with VQ based modelling technique. This work FIST has also been analysed by inducing disturbance in the form of speech interference from their own twin pairs and Gamma-tone energy feature with VQ based modelling technique performs better for twin identification. A high claim of 99.625% and 95.0625% accuracy has been achieved by employing decision level fusion classifier.