In this study, we present SeMaScore, generated using a segment-wise mapping
and scoring algorithm that serves as an evaluation metric for automatic speech
recognition tasks. SeMaScore leverages both the error rate and a more robust
similarity score. We show that our algorithm's score generation improves upon
the state-of-the-art BERTscore. Our experimental results show that SeMaScore
corresponds well with expert human assessments, signal-to-noise ratio levels,
and other natural language metrics. We outperform BERTscore by 41x in metric
computation speed. Overall, we demonstrate that SeMaScore serves as a more
dependable evaluation metric, particularly in real-world situations involving
atypical speech patterns.

本研究提出了 SeMaScore，它使用基于片段的映射和评分算法生成，并作为自动语音识别任务的评估指标。SeMaScore 利用错误率和更稳健的相似性得分，实验证明我们算法的评分生成优于现有的 BERTscore。我们的实验结果显示 SeMaScore 与专家人工评估、信噪比水平和其他自然语言指标相符。我们在计算速度上比 BERTscore 提高了 41 倍。总体而言，我们证明 SeMaScore 在涉及非典型语音模式的真实世界情况下是一种更可靠的评估指标。

SeMaScore：一种用于自动语音识别任务的新评估指标

SeMaScore : a new evaluation metric for automatic speech recognition  tasks

We propose a new problem formulation and a corresponding evaluation framework
to advance research on unsupervised domain adaptation for semantic image
segmentation. The overall goal is fostering the development of adaptive
learning systems that will continuously learn, without supervision, in
ever-changing environments. Typical protocols that study adaptation algorithms
for segmentation models are limited to few domains, adaptation happens offline,
and human intervention is generally required, at least to annotate data for
hyper-parameter tuning. We argue that such constraints are incompatible with
algorithms that can continuously adapt to different real-world situations. To
address this, we propose a protocol where models need to learn online, from
sequences of temporally correlated images, requiring continuous, frame-by-frame
adaptation. We accompany this new protocol with a variety of baselines to
tackle the proposed formulation, as well as an extensive analysis of their
behaviors, which can serve as a starting point for future research.

提出了一种新的问题表述和相应的评估框架来推进无监督领域自适应语义图像分割的研究；介绍了一种在线学习的协议，旨在解决现有协议对于模型的领域局限性和离线学习性质导致算法难以在不断变化的环境中连续自适应的问题，并伴随着多种基准算法和广泛的行为分析。