479 citations · 1.2k across the 92 of their papers we have counts for
10 papers · 1 filter
Few-Shot Synthetic Accented Speech for ASR Fine-Tuning: What Helps and When?
Yurii Halychanskyi, Nimet Beyza Bozdag, Mark Hasegawa-Johnson +2
Synthetic accented speech is a promising way to improve automatic speech recognition (ASR) when real accented recordings are scarce. We ask what makes such data useful for ASR fine…
AVMeme Exam: A Multimodal Multilingual Multicultural Benchmark for LLMs' Contextual and Cultural Knowledge and Thinking
Xilin Jiang, Qiaolin Wang, Junkai Wu +30
Internet audio-visual clips convey meaning through time-varying sound and motion, which extend beyond what text alone can represent. To examine whether AI models can understand suc…
MetaSICL: Globalizing Auditory LLMs for Underserved Speakers and Languages via Meta Speech In-Context Learning
Haolong Zheng, Siyin Wang, Zengrui Jin +1
Generative AI for speech and audio is increasingly expected to serve users across languages, cultures, and communities, yet current auditory Large Language Models (LLMs) are still…
Robust Cross-Etiology and Speaker-Independent Dysarthric Speech Recognition
Satwinder Singh, Qianli Wang, Zihan Zhong +4
In this paper, we present a speaker-independent dysarthric speech recognition system, with a focus on evaluating the recently released Speech Accessibility Project (SAP-1005) datas…
Sound Tagging in Infant-centric Home Soundscapes
Mohammad Nur Hossain Khan, Jialu Li, Nancy L. McElwain +2
Certain environmental noises have been associated with negative developmental outcomes for infants and young children. Though classifying or tagging sound events in a domestic envi…
ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers
Kaizhi Qian, Yang Zhang, Heting Gao +5
Self-supervised learning in speech involves training a speech representation network on a large-scale unannotated speech corpus, and then applying the learned representations to do…
Discovering Phonetic Inventories with Crosslingual Automatic Speech Recognition
Piotr Żelasko, Siyuan Feng, Laureano Moro Velazquez +5
The high cost of data acquisition makes Automatic Speech Recognition (ASR) model training problematic for most existing languages, including languages that do not even have a writt…
Multi-Decoder DPRNN: High Accuracy Source Counting and Separation
Junzhe Zhu, Raymond Yeh, Mark Hasegawa-Johnson
We propose an end-to-end trainable approach to single-channel speech separation with unknown number of speakers. Our approach extends the MulCat source separation backbone with add…
Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separation
Po-Sen Huang, Minje Kim, Mark Hasegawa-Johnson +1
Monaural source separation is important for many real world applications. It is challenging because, with only a single channel of information available, without any constraints, a…
Open-loop multi-channel inversion of room impulse response
Bowon Lee, Camille Goudeseune, Mark A. Hasegawa-Johnson
This paper considers methods for audio display in a CAVE-type virtual reality theater, a 3 m cube with displays covering all six rigid faces. Headphones are possible since the user…