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Mobile phones are playing an important role in changing the socio-economic landscapes of emerging economies like India. A proper voice-based user authentication will help in many new mobile based applications including mobilecommerce and banking. We present our exploration and evaluation of an experimental set-up for user authentication in remote Indian villages using mobile phones and userselected multilingual spoken passwords. We also present an effective speaker recognition method using a set of novel features called Compressed Feature Dynamics (CFD) which capture the speaker-identity effectively from the speech dynamics contained in the spoken passwords. Early trials demonstrate the effectiveness of the proposed method in handling noisy cell-phone speech. Compared to conventional text-dependent speaker recognition methods, the proposed CFD method delivers competitive performance while significantly reducing storage and computational complexity -an advantage highly beneficial for cell-phone based deployment of such user authentication systems.

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We present an Audio-visual person authentication system which extracts several novel "VisualizedSpeech-Features" (VSF) from the spoken-password and multiple face profiles using a simple user-interface and combine these features to deliver high performance and resilience against imposter attacks. The spoken password is converted to a string of images formed by several visualized speech features. A compressed form of these VSFs preserves speaker identity in a compact manner. Simulation results on an in-house 210-user AV-user-ID database collected with wide variations of users in real-life office environments demonstrate separable distributions of client andimposter scores (0% EER), while offering low storage and computational complexities compared to conventional AV user-recognition methods.