Abstract
Humans exhibit a remarkable ability to reliably classify sound sources in the environment even in presence of high levels of noise. In contrast, most engineering systems suffer a drastic drop in performance when speech signals are corrupted with channel or background distortions. Our brains are equipped with elaborate machinery for speech analysis and feature extraction, which hold great lessons for improving the performance of automatic speech processing systems under adverse conditions. The work presented here explores a biologically-motivated multi-resolution speaker information representation obtained by performing an intricate yet computationally-efficient analysis of the information-rich spectro-temporal attributes of the speech signal. We evaluate the proposed features in a speaker verification task performed on NIST SRE 2010 data. The biomimetic approach yields significant robustness in presence of non-stationary noise and reverberation, offering a new framework for deriving reliable features for speaker recognition and speech processing.
Highlights
In addition to the intended message, human voice carries the unique imprint of a speaker
The results clearly demonstrate that the proposed cortical features provide substantially lower equal error rate (EER)
In this report, we explore the applicability of a multiresolution analysis of speech signals to automatic speaker verification (ASV)
Summary
In addition to the intended message, human voice carries the unique imprint of a speaker. Just like fingerprints and faces, voice prints are biometric markers with tremendous potential for forensic, military, and commercial applications [1]. Despite enormous advances in computing technology over the last few decades, automatic speaker verification (ASV) systems still rely heavily on training data collected in controlled environments, and most systems face a rapid degradation in performance when operating under previously unseen conditions (e.g. channel mismatch, environmental noise, or reverberation). Human perception of speech and ability to identify sound sources (including voices) is quite remarkable even at relatively high distortion levels [2]. The pursuit of human-like recognition capabilities has spurred great interest in understanding how humans perceive and process speech signals.
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