5 citations · 10 across the 14 of their papers we have counts for
6 papers · 1 filter
Who Spoke What When? Evaluating Spoken Language Models for Conversational ASR with Semantic and Overlap-Aware Metrics
Naohiro Tawara, Samuele Cornell, Alexander Polok +3
Conversational automatic speech recognition remains challenging due to overlapping speech, far-field noise, and varying speaker counts. While recent LLM-based systems perform well…
DYNAC: Dynamic Vocabulary based Non-Autoregressive Contextualization for Speech Recognition
Yui Sudo, Yosuke Fukumoto, Muhammad Shakeel +3
Contextual biasing (CB) improves automatic speech recognition for rare and unseen phrases. Recent studies have introduced dynamic vocabulary, which represents context phrases as ex…
OWSM v4: Improving Open Whisper-Style Speech Models via Data Scaling and Cleaning
Yifan Peng, Shakeel Muhammad, Yui Sudo +4
The Open Whisper-style Speech Models (OWSM) project has developed a series of fully open speech foundation models using academic-scale resources, but their training data remains in…
Multi-Convformer: Extending Conformer with Multiple Convolution Kernels
Darshan Prabhu, Yifan Peng, Preethi Jyothi +1
Convolutions have become essential in state-of-the-art end-to-end Automatic Speech Recognition~(ASR) systems due to their efficient modelling of local context. Notably, its use in…
Improving ASR Contextual Biasing with Guided Attention
Jiyang Tang, Kwangyoun Kim, Suwon Shon +3
In this paper, we propose a Guided Attention (GA) auxiliary training loss, which improves the effectiveness and robustness of automatic speech recognition (ASR) contextual biasing…
Generative Context-aware Fine-tuning of Self-supervised Speech Models
Suwon Shon, Kwangyoun Kim, Prashant Sridhar +3
When performing tasks like automatic speech recognition or spoken language understanding for a given utterance, access to preceding text or audio provides contextual information ca…