1https://github.com/mjpost/sacrebleu

9

Table 3: The results of Automatic Speech Recognition (ASR), Speech-to-Text Translation (S2TT), Auto-
matic Audio Captioning (AAC), Speech Recognition with Word-level Timestamps (SRWT), Acoustic Scene
Classification (ASC), Speech Emotion Recognition (SER), Audio Question and Answering (AQA), Vocal
Sound Classification (VSC), and Music Note Analysis (MNA) tasks. For SRWT task, the results of Forced-
aligner (McAuliffe et al., 2017) are to predict the timestamps given the ground-truth transcripts, while
Paraformer-large-TP (Gao et al., 2023) and Qwen-audio tackle a more challenging scenario by directly gener-
ating sequences containing both transcriptions and timestamps.

Task

Dataset

Model

Performance

Librispeech

dev-clean | dev-other |
test-clean | test-other

Aishell1
dev | test

Aishell2

Mic | iOS | Android

CoVoST2
en-de | de-en |
en-zh | zh-en
CoVoST2

es-en | fr-en | it-en

Clotho

Industrial Data

CochlScene

TUT2017

Meld

ClothoAQA
Qwen-Audio