113 citations · 194 across the 17 of their papers we have counts for
5 papers · 1 filter
Detecting Speech Abnormalities with a Perceiver-based Sequence Classifier that Leverages a Universal Speech Model
Hagen Soltau, Izhak Shafran, Alex Ottenwess +7
We propose a Perceiver-based sequence classifier to detect abnormalities in speech reflective of several neurological disorders. We combine this classifier with a Universal Speech…
SLM: Bridge the thin gap between speech and text foundation models
Mingqiu Wang, Wei Han, Izhak Shafran +15
We present a joint Speech and Language Model (SLM), a multitask, multilingual, and dual-modal model that takes advantage of pretrained foundational speech and language models. SLM…
Efficient Adapters for Giant Speech Models
Nanxin Chen, Izhak Shafran, Yu Zhang +4
Large pre-trained speech models are widely used as the de-facto paradigm, especially in scenarios when there is a limited amount of labeled data available. However, finetuning all…
Speech-to-Text Adapter and Speech-to-Entity Retriever Augmented LLMs for Speech Understanding
Mingqiu Wang, Izhak Shafran, Hagen Soltau +4
Large Language Models (LLMs) have been applied in the speech domain, often incurring a performance drop due to misaligned between speech and language representations. To bridge thi…
Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages
Yu Zhang, Wei Han, James Qin +24
We introduce the Universal Speech Model (USM), a single large model that performs automatic speech recognition (ASR) across 100+ languages. This is achieved by pre-training the enc…