Characterization of Noise Associated with Forensic Speech Samples

Abstract

For speech enhancement, different methods have been developed in the past decades. This study has been carried out for characterization of various noises associated with forensic speech samples and their classification to find specific set of filtering technique for speech recognition and speaker identification. Noisy speech samples are collected from the exhibits received in case examination in the laboratory for this study. The experiment is performed in a two-fold way: enhancing the speech for (i) speech recognition and (ii) speaker identification. The original and simulated samples are subjected to various filtering techniques, namely, FFT Filter, noise reduction, noise gate, notch filter, bandpass, butterworth filter, digital equalizer and parametric equalizer for speech recognition. For speaker identification, noise reduction, noise gate, notch filter, bandpass and butterworth filter are applied to the noisy speech samples. Characterization of noise embedded with the noisy speech samples were attained based on the application of these filtering techniques and subsequent analysis performed on them using Computerized Speech Laboratory (CSL). For speech recognition, maximum SNR improvement was achieved by FFT filter on samples Noisy Speech-I (Direct Recording), Noisy Speech-II (Telephonic Landline Recording) and Noisy Speech-III (Mobile Phone Recording). The corresponding improvements in SNR for original and simulated samples were 3.81, 7.57, 5.62 dB and 4.39, 6.26, 5.57 dB respectively. FFT filter, when applied to the Noisy Speech-I, Noisy Speech-II and Noisy Speech-III of original noisy speech samples, have given an improvement of 75, 71 and 48%, whereas simulated noisy speech samples gave an improvement of 82, 78 and 52%. For speaker identification, maximum improvement was achieved by noise reduction filter when applied to the Noisy Speech-I, Noisy Speech-II and Noisy Speech-III of original noisy speech samples, have given an improvement of 60, 64 and 52% whereas simulated noisy speech samples gave an improvement of 64, 70 and 54%. Statistical study of improvised original noisy speech and simulated noisy speech samples after filtering have revealed the degree of efficiency of different filters for Speaker Identification and how far they are dependable in forensic adverse contexts. For Speech Recognition, the degree of efficiency of filters in enhancing the speech signal is found to be in a descending order; viz. FFT Filter, Noise reduction, Noise gate, Notch filter, Bandpass, Butterworth filter, Digital equalizer and Parametric equalizer. The degree of efficiency of filters in enhancing the speech signal for Speaker Identification is found to be in a descending order; viz. Noise Reduction, Noise Gate, Notch filter, Bandpass, and Butterworth filter.