The RedDots Data Collection for Speaker Recognition

Kong Aik Lee (1) , Anthony Larcher (2) , Guangsen Wang (1) , Patrick Kenny , Niko Brümmer , David van Leeuwen (3) , Hagai Aronowitz , Marcel Kockmann (4) , Carlos Vaquero , Bin Ma (1) , Haizhou Li , Themos Stafylakis , Jahangir Alam , Albert Swart , Javier Perez

1 A*STAR - Agency for science, technology and research [Singapore]
2 LIUM - Laboratoire d'Informatique de l'Université du Mans
3 Centre for Language Studies
4 VoiceTrust [Munich]

This paper describes data collection efforts conducted as part of the RedDots project which is dedicated to the study of speaker recognition under conditions where test utterances are of short duration and of variable phonetic content. At the current stage, we focus on English speakers, both native and non-native, recruited worldwide. This is made possible through the use of a recording front-end consisting of an application running on mobile devices communicating with a centralized web server at the back-end. Speech recordings are collected by having speakers read text prompts displayed on the screen of the mobile devices. We aim to collect a large number of sessions from each speaker over a long time span, typically one session per week over a one year period. The corpus is expected to include rich inter-speaker and intra-speaker variations, both intrinsic and extrinsic (that is, due to recording channel and acoustic environment).

Mots clés: corpus collection; crowd sourcing; speaker recognition

Domaines: Intelligence artificielle [cs.AI]

Kong Aik Lee, Anthony Larcher, Guangsen Wang, Patrick Kenny, Niko Brümmer, et al.. The RedDots Data Collection for Speaker Recognition. Interspeech 2015, Sep 2015, Dresden, Germany. ⟨hal-01818427⟩