Robust Methods for Text-Dependent Speaker Verification

In this work, we explore various noise robust techniques at different stages of a Text-Dependent Speaker Verification (TDSV) system. A speech-specific knowledge-based robust end points detection technique is used for noise compensation at signal level. Feature-level compensation is done by using robust features extracted from Hilbert Spectrum (HS) of the Intrinsic Mode Functions obtained from Modified Empirical Mode Decomposition of speech. We also explored a combined temporal and spectral speech enhancement technique prior to the end points detection for enhancing speech regions embedded in noise. All experimental studies are conducted using two databases, namely the RSR2015 and the IITG database. It is found that the use of robust end points detection improves the performance of the TDSV system compared to the energy-based end points detection in both clean and degraded speech conditions. Use of noise robust HS features augmented with Mel-frequency cepstral coefficients further improves the performance of the system. It is also found that the use of speech enhancement prior to signal and feature-level compensation results in further improvement in performance for the low SNR cases. The final combined system obtained by using three robust methods provides a relative improvement from 6 to 25% in terms of the EER, on the RSR2015 database corrupted with Babble noise of varying strength and by around from 30 to 45% relative improvement on the IITG database.