4 citations · 6 across the 5 of their papers we have counts for
5 papers
Single-channel speech enhancement using learnable loss mixup
Oscar Chang, Dung N. Tran, Kazuhito Koishida
Generalization remains a major problem in supervised learning of single-channel speech enhancement. In this work, we propose learnable loss mixup (LLM), a simple and effortless tra…
On Robustness to Missing Video for Audiovisual Speech Recognition
Oscar Chang, Otavio Braga, Hank Liao +2
It has been shown that learning audiovisual features can lead to improved speech recognition performance over audio-only features, especially for noisy speech. However, in many com…
Revisiting the Entropy Semiring for Neural Speech Recognition
Oscar Chang, Dongseong Hwang, Olivier Siohan
In streaming settings, speech recognition models have to map sub-sequences of speech to text before the full audio stream becomes available. However, since alignment information be…
Cascaded encoders for fine-tuning ASR models on overlapped speech
Richard Rose, Oscar Chang, Olivier Siohan
Multi-talker speech recognition (MT-ASR) has been shown to improve ASR performance on speech containing overlapping utterances from more than one speaker. Multi-talker models have…
Conformers are All You Need for Visual Speech Recognition
Oscar Chang, Hank Liao, Dmitriy Serdyuk +2
Visual speech recognition models extract visual features in a hierarchical manner. At the lower level, there is a visual front-end with a limited temporal receptive field that proc…