4 accepted papers
The speaker diarization task answers the question "who is speaking at a given time?". It represents valuable information for scene analysis in a domain such as robotics. In this paper, we introduce a temporal audio-visual fusion model for multiusers speaker diarization, with low computing requiremen…
Despite many recent advances for the design of dialogue systems, a true bottleneck remains the acquisition of data required to train its components. Unlike many other language processing applications, dialogue systems require interactions with users, therefore it is complex to develop them with pre-…
Many state-of-the-art solutions for the understanding of speech data have in common to be probabilistic and to rely on machine learning algorithms to train their models from large amount of data. The difficulty remains in the cost of collecting and annotating such data. Another point is the time for…
Many recent competitive state-of-the-art solutions for understanding of speech data have in common to be probabilistic and to rely on machine learning algorithms to train their models from large amount of data. The difficulty remains in the cost and time of collecting and annotating such data, but a…