no code implementations • 20 Dec 2023 • Jiachen Lian, Carly Feng, Naasir Farooqi, Steve Li, Anshul Kashyap, Cheol Jun Cho, Peter Wu, Robbie Netzorg, Tingle Li, Gopala Krishna Anumanchipalli
Dysfluent speech modeling requires time-accurate and silence-aware transcription at both the word-level and phonetic-level.
1 code implementation • 16 Oct 2023 • Cheol Jun Cho, Abdelrahman Mohamed, Shang-Wen Li, Alan W Black, Gopala K. Anumanchipalli
Data-driven unit discovery in self-supervised learning (SSL) of speech has embarked on a new era of spoken language processing.
no code implementations • 16 Oct 2023 • Cheol Jun Cho, Abdelrahman Mohamed, Alan W Black, Gopala K. Anumanchipalli
Self-Supervised Learning (SSL) based models of speech have shown remarkable performance on a range of downstream tasks.
no code implementations • 12 Aug 2023 • Cheol Jun Cho, Edward F. Chang, Gopala K. Anumanchipalli
The proposed framework learns more cross-trial consistent representations than the baselines, and when visualized, the manifold reveals shared neural trajectories across trials.
1 code implementation • 14 Feb 2023 • Peter Wu, Li-Wei Chen, Cheol Jun Cho, Shinji Watanabe, Louis Goldstein, Alan W Black, Gopala K. Anumanchipalli
To build speech processing methods that can handle speech as naturally as humans, researchers have explored multiple ways of building an invertible mapping from speech to an interpretable space.
1 code implementation • 21 Oct 2022 • Cheol Jun Cho, Peter Wu, Abdelrahman Mohamed, Gopala K. Anumanchipalli
Recent self-supervised learning (SSL) models have proven to learn rich representations of speech, which can readily be utilized by diverse downstream tasks.