The Given/New Distinction in Sentence Inversion: How Nonstandard Sentence Structure Affects Intonation
Session Number
Project ID: BHVSO 15
Advisor(s)
Dr. Jennifer Cole; Northwestern University
Discipline
Behavioral and Social Sciences
Start Date
22-4-2020 9:45 AM
End Date
22-4-2020 10:00 AM
Abstract
During conversation, speakers must evaluate what they are saying in relation to what has previously been said, the discourse context. Primarily, information is divided into two categories based on this relation: “given” and “new.” The “given/new” distinction is invoked in linguistic analysis in two ways. First is the claim that given information tends to be ordered before new information in the sequencing of sentence constituents. Second is the claim that new information is prosodically enhanced, e.g., through intonational features. While these claims imply a default prosodic highlight at the end of a phrase, there has been little research on these claims for sentences where word order does not respect that which is typical of English. This study examines the interaction between information status and word order and their combined effects on prosody in a speech production experiment. College volunteers will read aloud short narratives in which the first sentence sets up a discourse context and is followed by the second sentence which has either standard or inverted word order. Analysis of participants’ speech recordings will be based on perceived distinctions in prosodic marking of constituents and through pitch distinctions measured using the audio analysis software Praat. If prosodic marking is driven mainly by information status, we predict prosodic marking of discourse-new words, regardless of their position in the sentence. Otherwise, if prosodic marking is driven mainly by sentence position, we expect the sentence final word to be prosodically enhanced regardless of its information status. Findings from this experiment will inform our understanding of English prosody and its relationship with syntax and discourse structure and may even inform future work on computer-generated speech to enrich syntactic and prosodic variety and thereby improving naturalness.
The Given/New Distinction in Sentence Inversion: How Nonstandard Sentence Structure Affects Intonation
During conversation, speakers must evaluate what they are saying in relation to what has previously been said, the discourse context. Primarily, information is divided into two categories based on this relation: “given” and “new.” The “given/new” distinction is invoked in linguistic analysis in two ways. First is the claim that given information tends to be ordered before new information in the sequencing of sentence constituents. Second is the claim that new information is prosodically enhanced, e.g., through intonational features. While these claims imply a default prosodic highlight at the end of a phrase, there has been little research on these claims for sentences where word order does not respect that which is typical of English. This study examines the interaction between information status and word order and their combined effects on prosody in a speech production experiment. College volunteers will read aloud short narratives in which the first sentence sets up a discourse context and is followed by the second sentence which has either standard or inverted word order. Analysis of participants’ speech recordings will be based on perceived distinctions in prosodic marking of constituents and through pitch distinctions measured using the audio analysis software Praat. If prosodic marking is driven mainly by information status, we predict prosodic marking of discourse-new words, regardless of their position in the sentence. Otherwise, if prosodic marking is driven mainly by sentence position, we expect the sentence final word to be prosodically enhanced regardless of its information status. Findings from this experiment will inform our understanding of English prosody and its relationship with syntax and discourse structure and may even inform future work on computer-generated speech to enrich syntactic and prosodic variety and thereby improving naturalness.