Who Do I Sound Like? Showcasing Speaker Recognition Technology by YouTube Voice Search
This work addresses the problem of science communication for speaker recognition researchers by providing an engaging demo tool, though it is incremental as it applies existing methods to new data.
The authors tackled the challenge of making speaker recognition research accessible to the public by developing a web application that matches user voices to celebrity voices using YouTube data, achieving 93% accuracy and 665 ms per request in testing.
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.