most citedWenetSpeech-Yue: A Large-scale Cantonese Speech Corpus with Multi-dimensional Annotation

1 citations · 3 across the 18 of their papers we have counts for

collaborators
Showing eess.ASShow all

12 papers · 1 filter

eess.AS2026

RefTAG: Reflection-aware Temporal Audio Grounding in Large Audio Language Models

Yuhang Dai, Xin Shu, Zhennan Lin +5

Temporal audio grounding requires locating every occurrence of a queried target, making both occurrence counts and interval boundaries essential. We propose RefTAG, a post-training…

eess.AS2026

WenetSpeech-Min: A Large-Scale Minnan Speech Corpus with Dual Transcriptions for Dialectal Speech Processing

Haoyu Zhang, Chunjiang He, Hongtao Li +17

Progress in dialectal speech technology is hindered by the scarcity of large-scale, real-world corpora. For Minnan speech, existing resources remain limited, and few provide paired…

eess.AS2026

StreamTN: A Low-Latency Streaming Chinese Text Normalization Model for Streaming TTS in Dialogue Systems

Wenhao Li, Jinrui Liang, Haoyu Zhang +11

Text-to-Speech (TTS) is an essential module that provides spoken responses in a spoken dialogue system (SDS) centered on a large language model (LLM). To ensure accurate TTS synthe…

eess.AS2026

On-Policy Self-Distillation for Multi-Dialect ASR: Mastering Dialects, Retaining Mandarin

Shuiyuan Wang, Bingshen Mu, Pengshen Zhang +6

Recent large-scale ASR models already achieve strong Mandarin recognition accuracy and have some ability to recognize Chinese dialects. However, their dialect recognition accuracy…

eess.AS2026

SemBridge: Semantic Token Anchoring for Continuous-Latent Autoregressive Speech Generation

Hanke Xie, Haopeng Lin, Jiale Qian +13

Continuous-latent autoregressive speech generation has emerged as a promising alternative to discrete-token modeling by avoiding quantization loss and preserving richer acoustic in…

eess.AS2026

MSU-Bench: Towards Speaker-Centric Understanding in Conversational Multi-Speaker Scenarios

Zhaokai Sun, Shuai Wang, Zhennan Lin +6

Spoken Language Understanding (SLU) is moving from task-specific pipelines toward large audio language models (LALMs) that generate natural-language responses. However, existing sp…

eess.AS2026

Full-Duplex Interaction in Spoken Dialogue Systems: A Comprehensive Study from the ICASSP 2026 HumDial Challenge

Chengyou Wang, Hongfei Xue, Guojian Li +6

Full-duplex interaction, where speakers and listeners converse simultaneously, is a key element of human communication often missing from traditional spoken dialogue systems. These…

eess.AS2026

HumDial-EIBench: A Human-Recorded Multi-Turn Emotional Intelligence Benchmark for Audio Language Models

Shuiyuan Wang, Zhixian Zhao, Hongfei Xue +5

Evaluating the emotional intelligence (EI) of audio language models (ALMs) is critical. However, existing benchmarks mostly rely on synthesized speech, are limited to single-turn i…

eess.AS2026

OmniCodec: Low Frame Rate Universal Audio Codec with Semantic-Acoustic Disentanglement

Jingbin Hu, Haoyu Zhang, Dake Guo +10

Large Language Models (LLMs) have advanced audio generation through discrete representation learning. However, most existing neural codecs focus on speech and emphasize reconstruct…

eess.AS2026

VoiceSculptor: Your Voice, Designed By You

Jingbin Hu, Huakang Chen, Linhan Ma +19

Despite rapid progress in text-to-speech (TTS), open-source systems still lack truly instruction-following, fine-grained control over core speech attributes (e.g., pitch, speaking…

eess.AS2025

DialoSpeech: Dual-Speaker Dialogue Generation with LLM and Flow Matching

Hanke Xie, Dake Guo, Chengyou Wang +8

Recent advances in text-to-speech (TTS) synthesis, particularly those leveraging large language models (LLMs), have significantly improved expressiveness and naturalness. However,…

eess.AS2025

MSU-Bench: Towards Understanding the Conversational Multi-talker Scenarios

Shuai Wang, Zhaokai Sun, Zhennan Lin +3

Spoken Language Understanding (SLU) has progressed from traditional single-task methods to large audio language model (LALM) solutions. Yet, most existing speech benchmarks focus o…