EAGER: Matching Non-Native Transcribers to the Distinctive Features of the Language Transcribed

自动语音识别（ASR）系统必须使用数百小时的语音和同步文本转录进行训练。抄录这么多的话语超出了大多数语言社区的能力；因此，大多数语言不存在ASR系统。为了克服这个瓶颈，这个探索性的EAGER项目要求不懂特定语言的人把它转录成无意义的音节。当然，当人们试图用他们不理解的语言转录语音时，他们会犯错误。然而，这些错误有一定的模式，可以使用为电话和无线通信开发的解码策略来建模，并用于将每个转录任务路由给母语帮助他们完成任务的人。然后将产生的转录融合以恢复正确的转录。五种不同的语言将被测试，包括具有词汇语调的语言，以及具有各种辅音对比的语言，这些语言与英语非常不同。然后，生成的转录可以用所有五种语言训练ASR系统，并根据其训练这些系统的能力来评估研究的质量，而无需使用母语人士产生的转录。不匹配的众包被形式化为一个嘈杂的渠道；说话者用一串符号（音素）来编码意义，并不是所有的音素都能被感知者可靠地区分出来。每个转录者的第二语言语音感知模型可以使用感知同化模型初始化，然后特殊化。特别地，本提案寻求通过使用纠错代码来划分转录任务，然后将每个子任务分配给母语包含所要求的独特特征的转录员，从而增加不匹配众包的规模和鲁棒性。它还寻求在当前众包（标签噪声条件下函数的可学习性）和语法归纳（从一种语言到另一种语言的函数的可学习性）领域的交叉点开发新的理论，并在标签噪声条件下执行语法归纳。这个问题的某些方面存在初步界限；所提出的研究旨在发展更详细的理论结果，并测试和应用它们来确定为资源不足的语言创建可服务的ASR系统的可行性，而不必使用这些语言的流利使用者来转录这些语言的语音。

Automatic speech recognition (ASR) systems must be trained using hundreds of hours of speech, with synchronized text transcriptions. Transcribing that much speech is beyond the means of most language communities; therefore ASR systems do not exist for most languages. To overcome this bottleneck, this exploratory EAGER project asks people who don't understand a particular language to transcribe it as if they were listening to nonsense syllables. Of course, when people try to transcribe speech in a language they don't understand, they make mistakes. However there are patterns to those mistakes which can be modeled using decoding strategies developed for telephone and wireless communication, and used to route each transcription task to people whose native language helps them to perform it. The resulting transcriptions are then fused in order to recover correct transcriptions. Five different languages are to be tested, including languages with lexical tone, and languages with a variety of consonant contrasts very different from English. The resulting transcriptions can then train ASR systems in all five languages, and the quality of the research evaluated based on its ability to train those systems without using transcriptions produced by native speakers. Mismatched crowdsourcing is formalized as a noisy channel; the talker encodes meaning in a string of symbols (phonemes) not all of which are reliably distinguishable by the perceiver. Models of second-language speech perception for each transcriber can be initialized using a perceptual assimilation model, then specialized. In particular, this proposal seeks increases in the scale and robustness of mismatched crowdsourcing by using error-correcting codes to divide the transcription task, and by then distributing each sub-task to transcribers whose native language contains the distinctive feature requested. It also seeks to develop new theory at the intersection of the current fields of crowdsourcing (the learnability of a function under conditions of label noise) and grammar induction (the learnability of a function from one language to another), and to perform grammar induction under conditions of label noise. Preliminary bounds exist for some aspects of this problem; the proposed research is designed to develop more detailed theoretic results, and test and apply them to determine the feasibility of creating serviceable ASR systems for under-resourced languages without having to use fluent speakers of those languages to transcribe speech in those languages.