Title
Mind the gap. An investigation of omissions in audio description
Conference name
Arsad 2019
City
Country
Spain
Modalities
Date
19/04/2019
Abstract
There is broad consensus that audio description (AD) is a modality of intersemiotic translation, but there are different views in relation to how AD can be more precisely conceptualised. While Benecke (2014) characterises AD as ‘partial translation’, Braun (2016) hypothesises that what audio describers appear to ‘omit’ from their descriptions can normally be inferred by the audience, drawing on narrative cues from dialogue, mise-en-scène, kinesis, music or sound effects. This presentation reports on a study that is testing these hypotheses empirically.
Conducted as part of the EU-funded MeMAD project, our research aims to improve access to, and management of, audiovisual (AV) content through various methods, including by enhancing the automation of AV content description through a combination of approaches from computer vision, machine learning and human approaches to describing AV material. To this end, one of the MeMAD workstreams analyses how human audio descriptions approach the rendition of visually salient cues. We use a corpus of approx. 500 audio described film extracts to identify substantive visual elements, i.e. elements that can be considered essential for the construction of the filmic narrative, and analyse the corresponding audio descriptions in terms of how these elements are verbally represented. Where omissions in the audio description appear to occur, we conduct a qualitative analysis to establish whether the ‘omitted’ elements can be inferred from the co-text of the AD and/or from other cues that are accessible to visually impaired audiences (e.g. the film dialogue). Where possible, we establish the most likely source of these inferences.
In this presentation we outline the findings of the study and discuss their relevance, which we show to be twofold. Firstly, the study provides novel insights into a crucial aspect of AD practice and can inform approaches to training. Secondly, by highlighting how human audiences use their ability to draw inferences to build a coherent interpretation of what they perceive, the results of the study can also inform machine-based approaches to developing human-like descriptions of AV material.
Conducted as part of the EU-funded MeMAD project, our research aims to improve access to, and management of, audiovisual (AV) content through various methods, including by enhancing the automation of AV content description through a combination of approaches from computer vision, machine learning and human approaches to describing AV material. To this end, one of the MeMAD workstreams analyses how human audio descriptions approach the rendition of visually salient cues. We use a corpus of approx. 500 audio described film extracts to identify substantive visual elements, i.e. elements that can be considered essential for the construction of the filmic narrative, and analyse the corresponding audio descriptions in terms of how these elements are verbally represented. Where omissions in the audio description appear to occur, we conduct a qualitative analysis to establish whether the ‘omitted’ elements can be inferred from the co-text of the AD and/or from other cues that are accessible to visually impaired audiences (e.g. the film dialogue). Where possible, we establish the most likely source of these inferences.
In this presentation we outline the findings of the study and discuss their relevance, which we show to be twofold. Firstly, the study provides novel insights into a crucial aspect of AD practice and can inform approaches to training. Secondly, by highlighting how human audiences use their ability to draw inferences to build a coherent interpretation of what they perceive, the results of the study can also inform machine-based approaches to developing human-like descriptions of AV material.