Text-dependent automatic speaker verification naturally calls for the simultaneous verification of speaker identity and spoken content. These two tasks can be achieved with automatic speaker verification (ASV) and utterance verification (UV) technologies. While both have been addressed previously in the literature, a treatment of simultaneous speaker and utterance verification with a modern, standard database is so far lacking. This is despite the burgeoning demand for voice biometrics in a plethora of practical security applications. With the goal of improving overall verification performance, this paper reports different strategies for simultaneous ASV and UV in the context of short-duration, text-dependent speaker verification. Experiments performed on the recently released RedDots corpus are reported for three different ASV systems and four different UV systems. Results show that the combination of utterance verification with automatic speaker verification is (almost) universally beneficial with significant performance improvements being observed.

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The problem of text-dependent speaker verification under noisy conditions is becoming ever more relevant, due to increased usage for authentication in real-world applications. Classical methods for noise reduction such as spectral subtraction and Wiener filtering introduce distortion and do not perform well in this setting. In this work we compare the performance of different noise reduction methods under different noise conditions in terms of speaker verification when the text is known and the system is trained on clean data (mis-matched conditions). We furthermore propose a new approach based on dictionary-based noise reduction and compare it to the baseline methods.