Look who's talking: Detecting the dominant speaker in a cluttered scenario

n this work we propose a novel method to automatically detect and localise the dominant speaker in an enclosed scenario by means of audio and video cues. The underpinning idea is that gesturing means speaking, so observing motions means observing an audio signal. To the best of our knowledge state-of-the-art algorithms are focussed on stationary motion scenarios and close-up scenes where only one audio source exists, whereas we enlarge the extent of the method to larger field of views and cluttered scenarios including multiple non-stationary moving speakers. In such contexts, moving objects which are not correlated to the dominant audio may exist and their motion may incorrectly drive the audio-video (AV) correlation estimation. This suggests extra localisation data may be fused at decision level to avoid detecting false positives. In this work, we learn Mel-frequency cepstral coefficients (MFCC) coefficients and correlate them to the optical flow. We also exploit the audio and video signals to estimate the position of the actual speaker, narrowing down the visual space of search, hence reducing the probability of incurring in a wrong voice-to-pixel region association. We compare our work with a state-of-the-art existing algorithm and show on real datasets a 36% precision improvement in localising a moving dominant speaker through occlusions and speech interferences.