Final: xdiar: explainability for diarization

Speaker diarization aims at answering the question of “who speaks when” in a recording. It is a key task for many speech technologies such as automatic speech recognition (ASR), speaker identification and dialog monitoring in different multi-speaker scenarios, including TV/radio, meet- ings, and medical conversations. In many domains, such as health or human-machine interactions, the prediction of speaker segments is not enough and it is necessary to include additional para- linguistic information (age, gender, emotional state, speech pathology, etc.). However, most existing real-world applications are based on mono-modal modules trained separately, thus resulting in sub-optimal solutions. In addition, the current trend for explainable AI is a vital process for transparency of decision-making with machine learning: the user (a doctor, a judge, or a human scientist) has to justify the choice made on the basis of the system output.

This project aims at converting these outputs into interpretable clues (mispronounced phonemes, low speech rate, etc.) which explains the automatic diarization. While the question of simultaneously performed speech recognition and speaker diarization has been addressed under JSALT 2020, this proposal intends to develop a multi-task diarization system based on a joint latent representation of speaker and para-linguistic information. The latent representation embeds multiple modalities such as acoustic and linguistic or vision. This joint embedding space will be projected into a sparse and non-negative space in which all dimensions are interpretable by design. In the end, the diarization output will be a rich segmentation where speech segments are characterized with multiple labels, and interpretable attributes derived from the latent space.

Tags: deep nets ia information retrieval informatique jsalt linear algebra nlp workshop