Automatic speech recognition (ASR) meets more informal and free-form input
data as voice user interfaces and conversational agents such as the voice
assistants such as Alexa, Google Home, etc., gain popularity. Conversational
speech is both the most difficult and environmentally relevant sort of data for
speech recognition. In this paper, we take a linguistic perspective, and take
the French language as a case study toward disambiguation of the French
homophones. Our contribution aims to provide more insight into human speech
transcription accuracy in conditions to reproduce those of state-of-the-art ASR
systems, although in a much focused situation. We investigate a case study
involving the most common errors encountered in the automatic transcription of
French language.

本文以法语为案例研究，从语言学的角度探讨自动语音识别系统在复杂语境下的识别准确度，并解决了法语同音词的歧义问题，提高了法语语音转写准确度。

对话语言的自动语音识别系统评估：语言学视角

Evaluation of Automated Speech Recognition Systems for Conversational Speech: A Linguistic Perspective

The "Switchboard benchmark" is a very well-known test set in automatic speech
recognition (ASR) research, establishing record-setting performance for systems
that claim human-level transcription accuracy. This work highlights
lesser-known practical considerations of this evaluation, demonstrating major
improvements in word error rate (WER) by correcting the reference
transcriptions and deviating from the official scoring methodology. In this
more detailed and reproducible scheme, even commercial ASR systems can score
below 5% WER and the established record for a research system is lowered to
2.3%. An alternative metric of transcript precision is proposed, which does not
penalize deletions and appears to be more discriminating for human vs. machine
performance. While commercial ASR systems are still below this threshold, a
research system is shown to clearly surpass the accuracy of commercial human
speech recognition. This work also explores using standardized scoring tools to
compute oracle WER by selecting the best among a list of alternatives. A phrase
alternatives representation is compared to utterance-level N-best lists and
word-level data structures; using dense lattices and adding out-of-vocabulary
words, this achieves an oracle WER of 0.18%.

该研究探讨了在自动语音识别（ASR）研究中备受关注的‘Switchboard benchmark’数据集的一些实用考虑，并提出了修正参考转录和改变评分方法的更详细和可重复的方案。通过这种方案，甚至商业 ASR 系统的 WER 得分也可以低于 5％，研究系统的表现记录下降至 2.3％。此外，还探索了使用标准化评分工具计算 oracle WER 的方法，并比较了短语备选项表示和 utterance/word-level 的结构。最终，使用密集网络和添加字典外单词的方法实现了 0.18％的 oracle WER。