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

43 citations · 102 across the 48 of their papers we have counts for

collaborators
Showing 2026Show all

6 papers · 1 filter

cs.CL2026

NemotronLabs VoiceChat: An Open Full-duplex Speech-to-Speech Model with Tool Calling Capabilities

Jagadeesh Balam, Travis Bartley, Edresson Casanova +52

We introduce NemotronLabs VoiceChat, an open full-duplex speech-to-speech model with native tool-calling capabilities. NemotronLabs VoiceChat combines a streaming speech encoder an…

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…

cs.CL2026

Voice Memory for Agentic Speech Recognition

Chao-Han Huck Yang, Zih-Ching Chen, Piotr Zelasko +3

We present Voice Memory, a inference-only scheme for agentic speech recognition: at stream time, a frozen corrector reads a single per-domain memory.md and decides per utterance wh…

cs.CL2026

Preference-ASR: A Preference-Aware Test Set for Benchmarking ASR in the Era of Speech LLMs

Nithin Rao Koluguri, Sasha Meister, Nikolay Karpov +4

Popular ASR test sets adopt inconsistent conventions for numbers, disfluencies, entities, and casing, while standard normalizers erase the format distinctions users care about. Cur…

cs.LG2026

Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence

NVIDIA, :, Amala Sanjay Deshmukh +204

We introduce Nemotron 3 Nano Omni, the latest model in the Nemotron multimodal series and the first to natively support audio inputs alongside text, images, and video. Nemotron 3 N…

cs.LG2026

Chunk-wise Attention Transducers for Fast and Accurate Streaming Speech-to-Text

Hainan Xu, Vladimir Bataev, Travis M. Bartley +1

We propose Chunk-wise Attention Transducer (CHAT), a novel extension to RNN-T models that processes audio in fixed-size chunks while employing cross-attention within each chunk. Th…