6 citations · 10 across the 13 of their papers we have counts for
17 papers
Gemini Embedding 2: A Native Multimodal Embedding Model from Gemini
Madhuri Shanbhogue, Zhe Li, Shanfeng Zhang +86
We introduce Gemini Embedding 2, a native multimodal embedding model that allows embedding video, audio, image, and text modalities in a unified representation space. We leverage t…
MedASR: An Open-Source Model for High-Accuracy Medical Dictation
Ke Wu, Ehsan Variani, Tom Bagby +2
We present MedASR, an open-source 105M-parameter model engineered for high-accuracy medical dictation. Prioritizing a "small, fast, and accurate" design, MedASR addresses 3 core pi…
Benchmarking LLMs on the Massive Sound Embedding Benchmark (MSEB)
Cyril Allauzen, Tom Bagby, Georg Heigold +2
The Massive Sound Embedding Benchmark (MSEB) has emerged as a standard for evaluating the functional breadth of audio models. While initial baselines focused on specialized encoder…
Massive Sound Embedding Benchmark (MSEB)
Georg Heigold, Ehsan Variani, Tom Bagby +4
Audio is a critical component of multimodal perception, and any truly intelligent system must demonstrate a wide range of auditory capabilities. These capabilities include transcri…
LAST: Scalable Lattice-Based Speech Modelling in JAX
Ke Wu, Ehsan Variani, Tom Bagby +1
We introduce LAST, a LAttice-based Speech Transducer library in JAX. With an emphasis on flexibility, ease-of-use, and scalability, LAST implements differentiable weighted finite s…
JEIT: Joint End-to-End Model and Internal Language Model Training for Speech Recognition
Zhong Meng, Weiran Wang, Rohit Prabhavalkar +7
We propose JEIT, a joint end-to-end (E2E) model and internal language model (ILM) training method to inject large-scale unpaired text into ILM during E2E training which improves ra…