41 citations · 52 across the 9 of their papers we have counts for
10 papers
Gemma 4 Technical Report
Gemma Team, Sherif El Abd, Vaibhav Aggarwal +320
We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemm…
Neural approaches to spoken content embedding
Shane Settle
Comparing spoken segments is a central operation to speech processing. Traditional approaches in this area have favored frame-level dynamic programming algorithms, such as dynamic…
What Do Self-Supervised Speech Models Know About Words?
Ankita Pasad, Chung-Ming Chien, Shane Settle +1
Many self-supervised speech models (S3Ms) have been introduced over the last few years, improving performance and data efficiency on various speech tasks. However, these empirical…
Acoustic span embeddings for multilingual query-by-example search
Yushi Hu, Shane Settle, Karen Livescu
Query-by-example (QbE) speech search is the task of matching spoken queries to utterances within a search collection. In low- or zero-resource settings, QbE search is often address…
Whole-Word Segmental Speech Recognition with Acoustic Word Embeddings
Bowen Shi, Shane Settle, Karen Livescu
Segmental models are sequence prediction models in which scores of hypotheses are based on entire variable-length segments of frames. We consider segmental models for whole-word ("…
Multilingual Jointly Trained Acoustic and Written Word Embeddings
Yushi Hu, Shane Settle, Karen Livescu
Acoustic word embeddings (AWEs) are vector representations of spoken word segments. AWEs can be learned jointly with embeddings of character sequences, to generate phonetically mea…