3 citations · 8 across the 6 of their papers we have counts for
9 papers
The Potential of Neural Speech Synthesis-based Data Augmentation for Personalized Speech Enhancement
Anastasia Kuznetsova, Aswin Sivaraman, Minje Kim
With the advances in deep learning, speech enhancement systems benefited from large neural network architectures and achieved state-of-the-art quality. However, speaker-agnostic me…
Adapting Speech Separation to Real-World Meetings Using Mixture Invariant Training
Aswin Sivaraman, Scott Wisdom, Hakan Erdogan +1
The recently-proposed mixture invariant training (MixIT) is an unsupervised method for training single-channel sound separation models in the sense that it does not require ground-…
Zero-Shot Personalized Speech Enhancement through Speaker-Informed Model Selection
Aswin Sivaraman, Minje Kim
This paper presents a novel zero-shot learning approach towards personalized speech enhancement through the use of a sparsely active ensemble model. Optimizing speech denoising sys…
Personalized Speech Enhancement through Self-Supervised Data Augmentation and Purification
Aswin Sivaraman, Sunwoo Kim, Minje Kim
Training personalized speech enhancement models is innately a no-shot learning problem due to privacy constraints and limited access to noise-free speech from the target user. If t…
Detecting Extraneous Content in Podcasts
Sravana Reddy, Yongze Yu, Aasish Pappu +3
Podcast episodes often contain material extraneous to the main content, such as advertisements, interleaved within the audio and the written descriptions. We present classifiers th…
Sparse Mixture of Local Experts for Efficient Speech Enhancement
Aswin Sivaraman, Minje Kim
In this paper, we investigate a deep learning approach for speech denoising through an efficient ensemble of specialist neural networks. By splitting up the speech denoising task i…