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Capitalization and punctuation are important cues for comprehending writtentexts and conversational transcripts. Yet, many ASR systems do not producepunctuated and case-formatted speech transcripts. We propose to use amulti-task system that can exploit the relations between casing and punctuationto improve their prediction performance. Whereas text data for predictingpunctuation and truecasing is seemingly abundant, we argue that written textresources are inadequate as training data for conversational models. Wequantify the mismatch between written and conversational text domains bycomparing the joint distributions of punctuation and word cases, and by testingour model cross-domain. Further, we show that by training the model in thewritten text domain and then transfer learning to conversations, we can achievereasonable performance with less data.

Joint prediction of truecasing and punctuation for conversational speech in low-resource scenarios