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的文本转录进行培训。转录如此多的语音超出了大多数语言社区的能力范围；因此，大多数语言都不存在ASR系统。为了克服这一瓶颈，这个探索性的、渴望的项目要求不懂一种特定语言的人转录它，就像他们在听无意义的音节一样。当然，当人们试图用他们不懂的语言转录演讲时，他们会犯错误。然而，这些错误有一些模式，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发送给母语帮助他们执行该任务的人。然后融合得到的转录，以恢复正确的转录。将测试五种不同的语言，包括具有词汇语调的语言，以及具有与英语非常不同的辅音对比的语言。然后，产生的转录可以训练所有五种语言的ASR系统，研究的质量是根据它训练这些系统的能力而不使用母语者产生的转录来评估的。不匹配的众包被形式化为一个嘈杂的通道；说话者将意思编码在一串符号(音素)中，而不是所有符号都能被接受者可靠地区分出来。每个转录者的第二语言语音感知模型可以使用知觉同化模型进行初始化，然后进行专门化。特别是，这项建议寻求增加不匹配的众包的规模和稳健性，方法是使用纠错码来划分转录任务，然后将每个子任务分配给母语包含所要求的独特功能的抄写员。它还试图在当前众包(在标签噪声条件下函数的可学习性)和语法归纳(函数从一种语言到另一种语言的可学习性)的交叉点上发展新的理论，并在标签噪声条件下进行语法归纳。这个问题的某些方面存在初步的界限；拟议的研究旨在开发更详细的理论结果，并测试和应用这些结果，以确定为资源不足的语言创建可用的ASR系统的可行性，而不必使用流利的语言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.