Few VR applications and games implement captioning of speech and audio cues, which either inhibits or prevents access of their application by deaf or hard of hearing (DHH) users, new language learners, and other caption users. Additionally, little to no guidelines exist on how to implement live captioning on VR headsets and how it may differ from traditional television captioning. To help fill the void of information behind user preferences of different VR captioning styles, we conducted a study with eight DHH participants to test three caption movement behaviors (head-locked, lag, and appear- locked) while watching live-captioned, single-speaker presentations in VR. Participants answered a series of Likert scale and open-ended questions about their experience. Participants’ preferences were split, but most participants reported feeling comfortable with using live captions in VR and enjoyed the experience. When participants ranked the caption behaviors, there was almost an equal divide between the three types tested. IPQ results indicated each behavior had similar immersion ratings, however participants found head-locked and lag captions more user-friendly than appear-locked captions. We suggest that participants may vary in caption preference depending on how they use captions, and that providing opportunities for caption customization is best.
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Using BERT Embeddings to Model Word Importance in Conversational Transcripts for Deaf and Hard of Hearing Users
Deaf and hard of hearing individuals regularly rely on captioning while watching live TV. Live TV captioning is evaluated by regulatory agencies using various caption evaluation metrics. However, caption evaluation metrics are often not informed by preferences of DHH users or how meaningful the captions are. There is a need to construct caption evaluation metrics that take the relative importance of words in transcript into account. We conducted correlation analysis between two types of word embeddings and human-annotated labelled word-importance scores in existing corpus. We found that normalized contextualized word embeddings generated using BERT correlated better with manually annotated importance scores than word2vec-based word embeddings. We make available a pairing of word embeddings and their human-annotated importance scores. We also provide proof-of-concept utility by training word importance models, achieving an F1-score of 0.57 in the 6-class word importance classification task.
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- Award ID(s):
- 2125362
- PAR ID:
- 10343297
- Date Published:
- Journal Name:
- Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion
- Page Range / eLocation ID:
- 35 to 40
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Few VR applications and games implement captioning of speech and audio cues, which either inhibits or prevents access of their application by deaf or hard of hearing (DHH) users, new language learners, and other caption users. Additionally, little to no guidelines exist on how to implement live captioning on VR headsets and how it may differ from traditional television captioning. To help fill the void of information behind user preferences of different VR captioning styles, we conducted a study with eight DHH participants to test three caption movement behaviors (head-locked, lag, and appearlocked) while watching live-captioned, single-speaker presentations in VR. Participants answered a series of Likert scale and open-ended questions about their experience. Participants’ preferences were split, but most participants reported feeling comfortable with using live captions in VR and enjoyed the experience. When participants ranked the caption behaviors, there was almost an equal divide between the three types tested. IPQ results indicated each behavior had similar immersion ratings, however participants found head-locked and lag captions more user-friendly than appear-locked captions. We suggest that participants may vary in caption preference depending on how they use captions, and that providing opportunities for caption customization is bestmore » « less
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Object proposal generation serves as a standard pre-processing step in Vision-Language (VL) tasks (image captioning, visual question answering, etc.). The performance of object proposals generated for VL tasks is currently evaluated across all available annotations, a protocol that we show is misaligned - higher scores do not necessarily correspond to improved performance on downstream VL tasks. Our work serves as a study of this phenomenon and explores the effectiveness of semantic grounding to mitigate its effects. To this end, we propose evaluating object proposals against only a subset of available annotations, selected by thresholding an annotation importance score. Importance of object annotations to VL tasks is quantified by extracting relevant semantic information from text describing the image. We show that our method is consistent and demonstrates greatly improved alignment with annotations selected by image captioning metrics and human annotation when compared against existing techniques. Lastly, we compare current detectors used in the Scene Graph Generation (SGG) benchmark as a use case, which serves as an example of when traditional object proposal evaluation techniques are misaligned.more » « less
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Segmental models are sequence prediction models in which scores of hypotheses are based on entire variable-length segments of frames. We consider segmental models for whole-word ("acoustic-to-word") speech recognition, with the feature vectors defined using vector embeddings of segments. Such models are computationally challenging as the number of paths is proportional to the vocabulary size, which can be orders of magnitude larger than when using subword units like phones. We describe an efficient approach for end-to-end whole-word segmental models, with forward-backward and Viterbi decoding performed on a GPU and a simple segment scoring function that reduces space complexity. In addition, we investigate the use of pre-training via jointly trained acoustic word embeddings (AWEs) and acoustically grounded word embeddings (AGWEs) of written word labels. We find that word error rate can be reduced by a large margin by pre-training the acoustic segment representation with AWEs, and additional (smaller) gains can be obtained by pre-training the word prediction layer with AGWEs. Our final models improve over prior A2W models.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18653/v1/2022.ltedi-1.5
