The transcription bottleneck is often cited as a major obstacle for efforts to document the world's endangered languages and supply them with language technologies. One solution is to extend methods from automatic speech recognition and machine translation, and recruit linguists to provide narrow phonetic transcriptions and sentence-aligned translations. However, I believe that these approaches are not a good fit with the available data and skills, or with long-established practices that are essentially word-based. In seeking a more effective approach, I consider a century of transcription practice and a wide range of computational approaches, before proposing a computational model based on spoken term detection that I call “sparse transcription.” This represents a shift away from current assumptions that we transcribe phones, transcribe fully, and transcribe first. Instead, sparse transcription combines the older practice of word-level transcription with interpretive, iterative, and interactive processes that are amenable to wider participation and that open the way to new methods for processing oral languages.

中文翻译：

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稀疏转录

转录瓶颈通常被认为是记录世界濒危语言并为其提供语言技术的主要障碍。一种解决方案是扩展自动语音识别和机器翻译的方法，并招募语言学家提供窄语音转录和句子对齐翻译。但是，我认为这些方法不太适合可用的数据和技能，也不太适合基本上基于单词的长期实践。在寻求更有效的方法时，我考虑了一个世纪的转录实践和广泛的计算方法，然后提出了一个基于口语术语检测的计算模型，我称之为“稀疏转录”。这代表着我们从当前的假设转变，即我们转录电话，完全转录，并先转录。相反，稀疏转录将旧的单词级转录实践与解释、迭代和交互过程相结合，这些过程适合更广泛的参与，并为处理口语的新方法开辟了道路。