25 citations · 40 across the 24 of their papers we have counts for
4 papers · 1 filter
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…
Automatic Disfluency Detection from Untranscribed Speech
Amrit Romana, Kazuhito Koishida, Emily Mower Provost
Speech disfluencies, such as filled pauses or repetitions, are disruptions in the typical flow of speech. Stuttering is a speech disorder characterized by a high rate of disfluenci…
ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation
Yatong Bai, Trung Dang, Dung Tran +2
Diffusion models are instrumental in text-to-audio (TTA) generation. Unfortunately, they suffer from slow inference due to an excessive number of queries to the underlying denoisin…
Progressive Ensemble Distillation: Building Ensembles for Efficient Inference
Don Kurian Dennis, Abhishek Shetty, Anish Sevekari +2
We study the problem of progressive ensemble distillation: Given a large, pretrained teacher model , we seek to decompose the model into smaller, low-inference cost student mode…