BA-LR: Towards an Interpretable and Explainable Approach for Speaker Recognition

BA-LR: Towards an Interpretable and Explainable Approach for Speaker Recognition

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore an in-depth conference talk on interpretable and explainable approaches for speaker recognition presented by Imen Ben Amor from the Center for Language & Speech Processing (CLSP) at Johns Hopkins University. Delivered as part of the JSALT 2023 workshop held in Le Mans, France, this 75-minute presentation delves into the BA-LR methodology, aiming to enhance understanding and transparency in speaker recognition systems. Gain insights into cutting-edge research that bridges the gap between complex machine learning models and human-interpretable results in the field of speech processing.