activity
20202026
most citedCitrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition

43 citations · 66 across the 30 of their papers we have counts for

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22 papers · 1 filter

eess.AS2026

Pushing the Boundaries of Streaming Multi-Speaker ASR: A Systematic Study of Architectural Trade-offs

Taejin Park, Ivan Medennikov, Kunal Dhawan +3

Streaming multi-speaker ASR is a challenging task that must balance accuracy, latency, and efficiency while handling overlapping speech and maintaining coherent long-context modeli…

eess.AS2025

Streaming Sortformer: Speaker Cache-Based Online Speaker Diarization with Arrival-Time Ordering

Ivan Medennikov, Taejin Park, Weiqing Wang +5

This paper presents a streaming extension for the Sortformer speaker diarization framework, whose key property is the arrival-time ordering of output speakers. The proposed approac…

eess.AS2025

Speaker Targeting via Self-Speaker Adaptation for Multi-talker ASR

Weiqing Wang, Taejin Park, Ivan Medennikov +6

We propose a self-speaker adaptation method for streaming multi-talker automatic speech recognition (ASR) that eliminates the need for explicit speaker queries. Unlike conventional…

eess.AS2024

META-CAT: Speaker-Informed Speech Embeddings via Meta Information Concatenation for Multi-talker ASR

Jinhan Wang, Weiqing Wang, Kunal Dhawan +7

We propose a novel end-to-end multi-talker automatic speech recognition (ASR) framework that enables both multi-speaker (MS) ASR and target-speaker (TS) ASR. Our proposed model is…

eess.AS2024

Longer is (Not Necessarily) Stronger: Punctuated Long-Sequence Training for Enhanced Speech Recognition and Translation

Nithin Rao Koluguri, Travis Bartley, Hainan Xu +4

This paper presents a new method for training sequence-to-sequence models for speech recognition and translation tasks. Instead of the traditional approach of training models on sh…

eess.AS2024

DeSTA2: Developing Instruction-Following Speech Language Model Without Speech Instruction-Tuning Data

Ke-Han Lu, Zhehuai Chen, Szu-Wei Fu +5

Recent end-to-end speech language models (SLMs) have expanded upon the capabilities of large language models (LLMs) by incorporating pre-trained speech models. However, these SLMs…