5 citations · 11 across the 6 of their papers we have counts for
6 papers
Less is More: Data Curation Matters in Scaling Speech Enhancement
Chenda Li, Wangyou Zhang, Wei Wang +10
The vast majority of modern speech enhancement systems rely on data-driven neural network models. Conventionally, larger datasets are presumed to yield superior model performance,…
Dynamic-SUPERB Phase-2: A Collaboratively Expanding Benchmark for Measuring the Capabilities of Spoken Language Models with 180 Tasks
Chien-yu Huang, Wei-Chih Chen, Shu-wen Yang +77
Multimodal foundation models, such as Gemini and ChatGPT, have revolutionized human-machine interactions by seamlessly integrating various forms of data. Developing a universal spo…
CMU's IWSLT 2024 Simultaneous Speech Translation System
Xi Xu, Siqi Ouyang, Brian Yan +5
This paper describes CMU's submission to the IWSLT 2024 Simultaneous Speech Translation (SST) task for translating English speech to German text in a streaming manner. Our end-to-e…
Aligning Speech to Languages to Enhance Code-switching Speech Recognition
Hexin Liu, Xiangyu Zhang, Haoyang Zhang +4
Code-switching (CS) refers to the switching of languages within a speech signal and results in language confusion for automatic speech recognition (ASR). To address language confus…
ESPnet-ST IWSLT 2021 Offline Speech Translation System
Hirofumi Inaguma, Brian Yan, Siddharth Dalmia +4
This paper describes the ESPnet-ST group's IWSLT 2021 submission in the offline speech translation track. This year we made various efforts on training data, architecture, and audi…
Sequential Multi-Frame Neural Beamforming for Speech Separation and Enhancement
Zhong-Qiu Wang, Hakan Erdogan, Scott Wisdom +5
This work introduces sequential neural beamforming, which alternates between neural network based spectral separation and beamforming based spatial separation. Our neural networks…