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ASR for Under-Resourced Languages From Probabilistic Transcription

机译:来自概率转录的资源不足语言的ASR

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In many under-resourced languages it is possible to find text, and it is possible to find speech, but transcribed speech suitable for training automatic speech recognition (ASR) is unavailable. In the absence of native transcripts, this paper proposes the use of a probabilistic transcript: A probability mass function over possible phonetic transcripts of the waveform. Three sources of probabilistic transcripts are demonstrated. First, self-training is a well-established semisupervised learning technique, in which a cross-lingual ASR first labels unlabeled speech, and is then adapted using the same labels. Second, mismatched crowdsourcing is a recent technique in which nonspeakers of the language are asked to write what they hear, and their nonsense transcripts are decoded using noisy channel models of second-language speech perception. Third, EEG distribution coding is a new technique in which nonspeakers of the language listen to it, and their electrocortical response signals are interpreted to indicate probabilities. ASR was trained in four languages without native transcripts. Adaptation using mismatched crowdsourcing significantly outperformed self-training, and both significantly outperformed a cross-lingual baseline. Both EEG distribution coding and text-derived phone language models were shown to improve the quality of probabilistic transcripts derived from mismatched crowdsourcing.
机译:在许多资源不足的语言中,可以找到文本,也可以找到语音,但是没有适合训练自动语音识别(ASR)的转录语音。在没有原始成绩单的情况下,本文建议使用概率成绩单:波形可能的语音成绩单上的概率质量函数。证明了成绩单的三个来源。首先,自我训练是一种行之有效的半监督学习技术,其中,跨语言ASR首先标记未标记的语音,然后使用相同的标记进行调整。其次,不匹配的众包是一种最近的技术,在这种技术中,不讲该语言的人被要求写出他们听到的内容,然后使用第二语言语音感知的嘈杂通道模型对他们的废话成绩单进行解码。第三,EEG分布编码是一种新技术,其中该语言的讲者听不到,并且将其皮层电响应信号解释为指示概率。 ASR接受了四种语言的培训,没有本地成绩单。使用不匹配的众包进行的适应明显优于自我训练,并且两者均明显优于跨语言的基线。 EEG分配编码和文本衍生的电话语言模型都可以提高因不匹配的众包而产生的概率成绩单的质量。

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