Abstract
The emergence of smart home assistants increased the need for robust Far-Field Speaker Identification models. Speaker Identification enables the assistants to perform personalized tasks. Smart home assistants face very challenging speech conditions, including various room shapes and sizes, various distances of the speaker from the microphone, various types of distractor noises (TV in the background, air conditioner, fridge, babble speech of other speakers, etc.). This paper describes the use of Invariant Representation Learning (IRL) as a method aimed to increase the robustness of Speaker Identification models on Far-Field. We introduce three new versions of IRL: Text-Dependent IRL (TD-IRL), Text Independent IRL (TI-IRL), and Deep Features IRL (DF-IRL). We evaluate the IRL models performance and compare them to the base x-vector model. The various Far-Field scenarios are evaluated using VOiCES dataset - a dataset of simulated Far-Field recordings in four real furnished rooms. TD-IRL and DF-IRL improve the minDCF results on the far-field scenarios by an average of 36%, and TI-IRL improves it by 31% with respect to the baseline model.
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Shtrosberg, A., Villalba, J., Dehak, N., Cohen, A., Ben-Yair, B. (2021). Invariant Representation Learning for Robust Far-Field Speaker Recognition. In: Espinosa-Anke, L., Martín-Vide, C., Spasić, I. (eds) Statistical Language and Speech Processing. SLSP 2021. Lecture Notes in Computer Science(), vol 13062. Springer, Cham. https://doi.org/10.1007/978-3-030-89579-2_9
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