activity
20202026
most citedRecent Developments on ESPnet Toolkit Boosted by Conformer

40 citations · 110 across the 33 of their papers we have counts for

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Showing 2026Show all

8 papers · 1 filter

cs.SD2026

Luna-TTS Family Technical Report

Feng Yin, Shuai Shi, Junjie Zheng +19

Modern text-to-speech (TTS) is dominated by autoregressive (AR) codec language models, whose left-to-right decoding brings latency that grows with utterance length, error accumulat…

eess.AS2026

Towards Array-Invariant Speech Enhancement via Geometry-Aware Dynamic Convolution

Zhenglong Liu, Wangyou Zhang, Chenda Li +1

Multi-channel speech enhancement (SE) systems exhibit superior performance over single-channel methods but are constrained to fixed microphone array configurations. This restricts…

eess.AS2026

ESPnet3: Infrastructure for Scalable Speech and Audio Research in the Foundation Model Era

Masao Someki, Alexander Polok, Carlos Carvalho +14

Recent speech research involves increasingly large datasets, complex models, and diverse experimental workflows. However, existing frameworks require substantial engineering effort…

cs.SD2026

TF-MoE: Time-Frequency Mixture-of-Experts for Efficient Speech Separation

Qinzhe Hu, Chenda Li, Wangyou Zhang +3

Recent advances in speech separation (SS) have led to compact front-end models with small parameter sizes, yet their high computational cost remains a major barrier for deployment…

eess.AS2026

Representation-Regularized Convolutional Audio Transformer for Audio Understanding

Bing Han, Chushu Zhou, Yifan Yang +4

Bootstrap-based Self-Supervised Learning (SSL) has achieved remarkable progress in audio understanding. However, existing methods typically operate at a single level of granularity…

cs.SD2026

SLM-SS: Speech Language Model for Generative Speech Separation

Tianhua Li, Chenda Li, Wei Wang +4

Speech separation (SS) has advanced significantly with neural network-based methods, showing improved performance on signal-level metrics. However, these methods often struggle to…

cs.SD2026

UrgentMOS: Unified Multi-Metric and Preference Learning for Robust Speech Quality Assessment

Wei Wang, Wangyou Zhang, Chenda Li +12

Automatic speech quality assessment has become increasingly important as modern speech generation systems continue to advance, while human listening tests remain costly, time-consu…

eess.AS2026

ICASSP 2026 URGENT Speech Enhancement Challenge

Chenda Li, Wei Wang, Marvin Sach +8

The ICASSP 2026 URGENT Challenge advances the series by focusing on universal speech enhancement (SE) systems that handle diverse distortions, domains, and input conditions. This o…