Speech Recognition (ASR) results, as depicted in Table 3, where Qwen-Audio exhibits superior perfor-
mance compared to previous multi-task learning models. Specifically, it achieves a 2.0% and 4.2% WER
on the librispeech test-clean and test-other datasets, respectively. Similarly, the Chinese Mandarin ASR
results demonstrate Qwen-Audio’s competitive performance against previous approaches. To the best of
our knowledge, Qwen-Audio achieves state-of-the-art results on the Aishell1 dev and test sets. Furthermore,
we evaluate Qwen-Audio’s speech translation performance on the CoVoST2 dataset. The results reveal that
Qwen-Audio outperforms the baselines by a substantial margin across all seven translation directions.
Lastly, we analyze the performance of Qwen-Audio on various audio analysis tasks, including AAC, SWRT
ASC, SER, AQA, VSC, and MNA, as summarized in Table 3. Across these tasks, Qwen-Audio consistently
Qwen-Audio