Strategies to Improve Robustness of Target Speech Extraction to Enrollment Variations
Hiroshi Sato, Tsubasa Ochiai, Marc Delcroix, Keisuke Kinoshita, Takafumi Moriya, Naoki Makishima, Mana Ihori, Tomohiro Tanaka, Ryo Masumura · Interspeech 2022 · 2022
Target speech extraction is a technique to extract the target speaker's voice from mixture signals using a pre-recorded enrollment utterance that characterize the voice characteristics of the target speaker.One major difficulty of target speech extraction lies in handling variability in "intra-speaker" characteristics, i.e., characteristics mismatch between target speech and an enrollment utterance.While most conventional approaches focus on improving average performance given a set of enrollment utterances, here we propose to guarantee the worst performance, which we believe is of great practical importance.In this work, we propose an evaluation metric called worstenrollment source-to-distortion ratio (SDR) to quantitatively measure the robustness towards enrollment variations.We also introduce a novel training scheme that aims at directly optimizing the worst-case performance by focusing on training with difficult enrollment cases where extraction does not perform well.In addition, we investigate the effectiveness of auxiliary speaker identification loss (SI-loss) as another way to improve robustness over enrollments.Experimental validation reveals the effectiveness of both worst-enrollment target training and SI-loss training to improve robustness against enrollment variations, by increasing speaker discriminability.