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Representing 'how you say' with 'what you say': English corpus of focused speech and text reflecting corresponding implications

arXiv 2203.15483 ↗First posted 29 Mar 2022cs.CLcited by 1

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Abstract (arXiv)

In speech communication, how something is said (paralinguistic information) is as crucial as what is said (linguistic information). As a type of paralinguistic information, English speech uses sentence stress, the heaviest prominence within a sentence, to convey emphasis. While different placements of sentence stress communicate different emphatic implications, current speech translation systems return the same translations if the utterances are linguistically identical, losing paralinguistic information. Concentrating on focus, a type of emphasis, we propose mapping paralinguistic information into the linguistic domain within the source language using lexical and grammatical devices. This method enables us to translate the paraphrased text representations instead of the transcription of the original speech and obtain translations that preserve paralinguistic information. As a first step, we present the collection of an English corpus containing speech that differed in the placement of focus along with the corresponding text, which was designed to reflect the implied meaning of the speech. Also, analyses of our corpus demonstrated that mapping of focus from the paralinguistic domain into the linguistic domain involved various lexical and grammatical methods. The data and insights from our analysis will further advance research into paralinguistic translation. The corpus will be published via LDC and our website.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 3 of its 42 references that are in the map. First posted on arXiv 29 Mar 2022.

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References · 3

  1. 2020GECToR -- Grammatical Error Correction: Tag, Not Rewritecited by 177
  2. 2019Direct speech-to-speech translation with a sequence-to-sequence modelcited by 118
  3. 2019Leveraging Acoustic Cues and Paralinguistic Embeddings to Detect Expression from Voicecited by 7

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