Sistema semiautomático de reconhecimento de identidade vocal forense.

Abstract

Due to the development of telephone networks, the use of this environment to support criminals to commit crimes is increasingly common. Based, then, on the possibility of individualizing one person from their vocal characteristics, this work proposes using techniques for the semiautomatic vocal identity recognition of speakers in telephone environment, aiming help in criminal investigations, directing the attribution of voice authorship and, thus, suiting as evidence in forensic. For that purpose, are used DC offset, vocal detector activity, spectral subtraction, normalization and pre-emphasis such as pre-processing techniques of speech signal, which aim to minimize the negative effects that provides the telephone environment utterances transmitted by these means, reducing the errors in feature extraction and subsequently, in the patterns creation of each speaker. In order to optimize the processing efficiency and robustness to noise compared to other methods for feature extraction, Mel-Frequency Cepstral Coefficients (MFCC) was employed. To create the speakers patterns and classification, it was used the Gaussian Mixture Model (GMM), because provide better results when there is no dependence of text, due to the speakers are non-cooperative. Aiming at finding the best parameter setting for the semi-automatic system, experiments were performed considering an automatic vocal identity recognition system. In this way, it was possible reach to correct identification rate of up to 87.80%, with a confidence level of 98%. Lastly, the semiautomatic speaker identification system reached the probability of 99.95% that a given utterance belongs to a given speaker from a set of 30 suspects, using a confidence level of 98%. Thus, the proposed technique has enabled to provide, with a tax rate close to 100%, a subset of speakers suspects for subsequent forensic analysis.