Who Do I Sound like? Showcasing Speaker Recognition Technology by Youtube Voice Search
Ville Vestman, Bilal Soomro, Anssi Kanervisto, Ville Hautamäki, Tomi Kinnunen
Abstract
The popularization of science can often be disregarded by scientists as it may be challenging to put highly sophisticated research into words that general public can understand. This work aims to help presenting speaker recognition research to public by proposing a publicly appealing concept for showcasing recognition systems. We leverage data from YouTube and use it in a large-scale voice search web application that finds the celebrity voices that best match to the user's voice. The concept was tested in a public event as well as "in the wild" and the received feedback was mostly positive. The i-vector based speaker identification back end was found to be fast (665 ms per request) and had a high identification accuracy (93%) for the YouTube target speakers. To help other researchers to develop the idea further, we share the source codes of the web platform used for the demo at https://github.com/bilalsoomro/speech-demo-platform.
BibTeX
@inproceedings{icassp2019_whodoisoundlikes,
title = {Who Do I Sound like? Showcasing Speaker Recognition Technology by Youtube Voice Search},
author = {Ville Vestman and Bilal Soomro and Anssi Kanervisto and Ville Hautamäki and Tomi Kinnunen},
booktitle = {ICASSP 2019},
year = {2019}
}