  Speaker clustering is the task of differentiating speakers in a recording. In
a way, the aim is to answer "who spoke when" in audio recordings. A common
method used in industry is feature extraction directly from the recording
thanks to MFCC features, and by using well-known techniques such as Gaussian
Mixture Models (GMM) and Hidden Markov Models (HMM). In this paper, we studied
neural networks (especially CNN) followed by clustering and audio processing in
the quest to reach similar accuracy to state-of-the-art methods.
