Utterance Verification for Text-Dependent Speaker Recognition: a Comparative Assessment Using the RedDots Corpus
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CitationKinnunen, Tomi. Sahidullah, Md. Kukanov, Ivan. Delgado, Hector. Todisco, Massimiliano. Sarkar, Achintya. Thomsen, Nicolai Baek. Hautamäki, Ville. Evans, Nicholas. Tan, Zheng-Hua. (2016). Utterance Verification for Text-Dependent Speaker Recognition: a Comparative Assessment Using the RedDots Corpus. Prodeedings of The 17th Annual Conference of the International Speech Communication Association, 430-434. 10.21437/Interspeech.2016-1125.
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.