Automatic speech recognition (ASR) outcomes serve as input for downstream
tasks, substantially impacting the satisfaction level of end-users. Hence, the
diagnosis and enhancement of the vulnerabilities present in the ASR model bear
significant importance. However, traditional evaluation methodologies of ASR
systems generate a singular, composite quantitative metric, which fails to
provide comprehensive insight into specific vulnerabilities. This lack of
detail extends to the post-processing stage, resulting in further obfuscation
of potential weaknesses. Despite an ASR model's ability to recognize utterances
accurately, subpar readability can negatively affect user satisfaction, giving
rise to a trade-off between recognition accuracy and user-friendliness. To
effectively address this, it is imperative to consider both the speech-level,
crucial for recognition accuracy, and the text-level, critical for
user-friendliness. Consequently, we propose the development of an Error
Explainable Benchmark (EEB) dataset. This dataset, while considering both
speech- and text-level, enables a granular understanding of the model's
shortcomings. Our proposition provides a structured pathway for a more
`real-world-centric' evaluation, a marked shift away from abstracted,
traditional methods, allowing for the detection and rectification of nuanced
system weaknesses, ultimately aiming for an improved user experience.

通过提出一个 Speech 和 Text 综合考虑的 Error Explainable Benchmark 数据集，我们能够更全面地了解 ASR 模型的不足之处，消除系统的弱点，从而提高用户体验。

实用自动语音识别与后处理：可解释错误基准指南的呼吁

Toward Practical Automatic Speech Recognition and Post-Processing: a  Call for Explainable Error Benchmark Guideline

Recent advances in vision-language models like Stable Diffusion have shown
remarkable power in creative image synthesis and editing.However, most existing
text-to-image editing methods encounter two obstacles: First, the text prompt
needs to be carefully crafted to achieve good results, which is not intuitive
or user-friendly. Second, they are insensitive to local edits and can
irreversibly affect non-edited regions, leaving obvious editing traces. To
tackle these problems, we propose a Zero-shot instructiON-guided local image
Editing approach, termed ZONE. We first convert the editing intent from the
user-provided instruction (e.g., ``make his tie blue") into specific image
editing regions through InstructPix2Pix. We then propose a Region-IoU scheme
for precise image layer extraction from an off-the-shelf segment model. We
further develop an edge smoother based on FFT for seamless blending between the
layer and the image.Our method allows for arbitrary manipulation of a specific
region with a single instruction while preserving the rest. Extensive
experiments demonstrate that our ZONE achieves remarkable local editing results
and user-friendliness, outperforming state-of-the-art methods.

基于 Zero-shot 指令引导的局部图像编辑方法 ZONE 通过转换用户提供的指令，实现了对特定图像区域的任意操作，同时保留其他区域的编辑友好性，展现了出色的局部编辑效果，优于现有的最先进方法。