1 citations · 1 across the 3 of their papers we have counts for
7 papers
Word Level Timestamp Generation for Automatic Speech Recognition and Translation
Ke Hu, Krishna Puvvada, Elena Rastorgueva +7
We introduce a data-driven approach for enabling word-level timestamp prediction in the Canary model. Accurate timestamp information is crucial for a variety of downstream tasks su…
SALM-Duplex: Efficient and Direct Duplex Modeling for Speech-to-Speech Language Model
Ke Hu, Ehsan Hosseini-Asl, Chen Chen +7
Spoken dialogue is an intuitive form of human-computer interaction, yet current speech language models often remain constrained to turn-based exchanges, lacking real-time adaptabil…
Training and Inference Efficiency of Encoder-Decoder Speech Models
Piotr Żelasko, Kunal Dhawan, Daniel Galvez +7
Attention encoder-decoder model architecture is the backbone of several recent top performing foundation speech models: Whisper, Seamless, OWSM, and Canary-1B. However, the reporte…
NeKo: Cross-Modality Post-Recognition Error Correction with Tasks-Guided Mixture-of-Experts Language Model
Yen-Ting Lin, Zhehuai Chen, Piotr Zelasko +11
Construction of a general-purpose post-recognition error corrector poses a crucial question: how can we most effectively train a model on a large mixture of domain datasets? The an…
VoiceTextBlender: Augmenting Large Language Models with Speech Capabilities via Single-Stage Joint Speech-Text Supervised Fine-Tuning
Yifan Peng, Krishna C. Puvvada, Zhehuai Chen +7
Recent studies have augmented large language models (LLMs) with speech capabilities, leading to the development of speech language models (SpeechLMs). Earlier SpeechLMs focused on…
EMMeTT: Efficient Multimodal Machine Translation Training
Piotr Żelasko, Zhehuai Chen, Mengru Wang +7
A rising interest in the modality extension of foundation language models warrants discussion on the most effective, and efficient, multimodal training approach. This work focuses…