Tanvina B. Patel

TL;DR: A detector based on combination of cochlear filter cepstral coefficients (CFCC) and change in instantaneous frequency (IF), (i.e., CFCCIF) to detect natural vs. spoofed speech for ASVspoof 2015 challenge is proposed.

TL;DR: A novel replay detector based on Variable length Teager Energy OperatorEnergy Separation Algorithm-Instantaneous Frequency Cosine Coefficients (VESA-IFCC) for the ASV spoof 2017 challenge is proposed and the performance of the proposed feature set is compared with the features developed for detecting synthetic and voice converted speech.

TL;DR: A consortium effort on building text to speech (TTS) systems for 13 Indian languages using the same common framework and the TTS systems are evaluated using Mean Opinion Score (DMOS) and Word Error Rate (WER).

TL;DR: It was observed that subband energy variations across CFCCIF when estimated by symmetric difference (CFCCIFS) gave better discriminative properties than CFCC IF, and VC speech is relatively difficult to detect than SS by the SSD system.

TL;DR: This work explores excitation source-based features, i.e., fundamental frequency (F0) contour and strength of excitation (SoE) at the glottis as discriminative features using GMM-based classification system and finds that source features captures complementary information than MFCC and CFCCIF used alone.