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eess.AS2022
Updating Only Encoders Prevents Catastrophic Forgetting of End-to-End ASR Models
Yuki Takashima, Shota Horiguchi, Shinji Watanabe +2
In this paper, we present an incremental domain adaptation technique to prevent catastrophic forgetting for an end-to-end automatic speech recognition (ASR) model. Conventional app…
eess.AS2021
Semi-Supervised Training with Pseudo-Labeling for End-to-End Neural Diarization
Yuki Takashima, Yusuke Fujita, Shota Horiguchi +3
In this paper, we present a semi-supervised training technique using pseudo-labeling for end-to-end neural diarization (EEND). The EEND system has shown promising performance compa…
eess.AS2019
Feature Enhancement with Deep Feature Losses for Speaker Verification
Saurabh Kataria, Phani Sankar Nidadavolu, Jesús Villalba +3
Speaker Verification still suffers from the challenge of generalization to novel adverse environments. We leverage on the recent advancements made by deep learning based speech enh…