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Replay attacks presents a great risk for Automatic Speaker Verification (ASV) system. In this paper, we propose a novel replay detector based on Variable length Teager Energy Operator-Energy Separation Algorithm-Instantaneous Frequency Cosine Coefficients (VESA-IFCC) for the ASV spoof 2017 challenge.The key idea here is to exploit the contribution of IF in each subband energy via ESA to capture possible changes in spectral envelope (due to transmission and channel characteristics of replay device) of replayed speech. The IF is computed from narrowband components of speech signal, and DCT is applied in IF to get proposed feature set. We compare the performance of the proposed VESA-IFCC feature set with the features developed for detecting synthetic and voice converted speech. This includes the CQCC, CFCCIF and prosody-based features. On the development set, the proposed VESA-IFCC features when fused at score-level with a variant of CFCCIF and prosodybased features gave the least EER of 0.12 %. On the evaluation set, this combination gave an EER of 18.33 %. However, post-evaluation results of challenge indicate that VESA-IFCC features alone gave the relatively least EER of 14.06 % (i.e., relatively 16.11 % less compared to baseline CQCC) and hence, is a very useful countermeasure to detect replay attacks.Variable length Teager Energy Operator (VTEO) is the modified version of the traditional TEO method [27]. TEO involves nonlinear operations on the signal, i.e, square of current sample and multiplication of previous and next sample, i.e., x(n − 1)

Abstract-In this paper, we propose to use modified Gammatone filterbank with Teager Energy Operator (TEO) for environmental sound classification (ESC) task. TEO can track energy as a function of both amplitude and frequency of an audio signal. TEO is better for capturing energy variations in the signal that is produced by a real physical system, such as, environmental sounds that contain amplitude and frequency modulations. In proposed feature set, we have used Gammatone filterbank since it represents characteristics of human auditory processing. Here, we have used two classifiers, namely, Gaussian Mixture Model (GMM) using cepstral features, and Convolutional Neural Network (CNN) using spectral features. We performed experiments on two datasets, namely, ESC-50, and UrbanSound8K. We compared TEO-based coefficients with Mel filter cepstral coefficients (MFCC) and Gammatone cepstral coefficients (GTCC), in which GTCC used mean square energy. Using GMM, the proposed TEO-based Gammatone Cepstral Coefficients (TEO-GTCC), and its score-level fusion with MFCC gave absolute improvement of 0.45 %, and 3.85 % in classification accuracy over MFCC on ESC-50 dataset. Similarly, on UrbanSound8K dataset the proposed TEO-GTCC, and its scorelevel fusion with GTCC gave absolute improvement of 1.40 %, and 2.44 % in classification accuracy over MFCC. Using CNN, the score-level fusion of Gammatone spectral coefficient (GTSC) and the proposed TEO-based Gammatone spectral coefficients (TEO-GTSC) gave absolute improvement of 14.10 %, and 14.52 % in classification accuracy over Mel filterbank energies (FBE) on ESC-50 and UrbanSond8K datasets, respectively. This shows that proposed TEO-based Gammatone features contain complementary information which is helpful in ESC task.

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