activity
20232026
most citedDeSTA2: Developing Instruction-Following Speech Language Model Without Speech Instruction-Tuning Data

15 citations · 49 across the 45 of their papers we have counts for

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18 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.AS2026

Reducing the Offline-Streaming Gap for Unified ASR Transducer with Consistency Regularization

Andrei Andrusenko, Vladimir Bataev, Lilit Grigoryan +3

Unification of automatic speech recognition (ASR) systems reduces development and maintenance costs, but training a single model to perform well in both offline and low-latency str…

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

DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model with Self-Generated Cross-Modal Alignment

Ke-Han Lu, Zhehuai Chen, Szu-Wei Fu +25

We introduce DeSTA2.5-Audio, a general-purpose Large Audio Language Model (LALM) designed for robust auditory perception and instruction-following. Recent LALMs augment Large Langu…

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…