7 papers
GigaSpeechBench: A Real-World Multilingual Speech-to-Text Benchmark
Yujie Tu, Yifan Yang, Tianrui Wang +36
While modern ASR systems achieve low error rates on high-resource benchmarks, such performance often overestimates real-world robustness. Existing evaluations address challenges in…
SOCO: Benchmarking Semantic Object Correspondence in Vision Foundation Models
Olaf Dünkel, Basavaraj Sunagad, Haoran Wang +3
Measuring structured object understanding in vision foundation models remains challenging due to inconsistent evaluation protocols and limited part-level supervision. Semantic corr…
A Unified and Reproducible Experimentation Framework for Speech Understanding
Jing Peng, Junhao Du, Chenghao Wang +21
Speech foundation models and Speech LLMs have advanced speech understanding, yet deployment-oriented model selection is hindered by non-comparable evaluations caused by mismatched…
Why Do Speech Language Models Fail to Generate Semantically Coherent Outputs? A Modality Evolving Perspective
Hankun Wang, Haoran Wang, Yiwei Guo +3
Although text-based large language models exhibit human-level writing ability and remarkable intelligence, speech language models (SLMs) still struggle to generate semantically coh…
BSCodec: A Band-Split Neural Codec for High-Quality Universal Audio Reconstruction
Haoran Wang, Jiatong Shi, Jinchuan Tian +3
Neural audio codecs have recently enabled high-fidelity reconstruction at high compression rates, especially for speech. However, speech and non-speech audio exhibit fundamentally…
Robust and Efficient Autoregressive Speech Synthesis with Dynamic Chunk-wise Prediction Policy
Bohan Li, Zhihan Li, Haoran Wang +5
Recently, autoregressive (AR) language models have emerged as a dominant approach in speech synthesis, offering expressive generation and scalable training. However, conventional A…