19 research outputs found
Multimodal Grounding for Sequence-to-Sequence Speech Recognition
Humans are capable of processing speech by making use of multiple sensory
modalities. For example, the environment where a conversation takes place
generally provides semantic and/or acoustic context that helps us to resolve
ambiguities or to recall named entities. Motivated by this, there have been
many works studying the integration of visual information into the speech
recognition pipeline. Specifically, in our previous work, we propose a
multistep visual adaptive training approach which improves the accuracy of an
audio-based Automatic Speech Recognition (ASR) system. This approach, however,
is not end-to-end as it requires fine-tuning the whole model with an adaptation
layer. In this paper, we propose novel end-to-end multimodal ASR systems and
compare them to the adaptive approach by using a range of visual
representations obtained from state-of-the-art convolutional neural networks.
We show that adaptive training is effective for S2S models leading to an
absolute improvement of 1.4% in word error rate. As for the end-to-end systems,
although they perform better than baseline, the improvements are slightly less
than adaptive training, 0.8 absolute WER reduction in single-best models. Using
ensemble decoding, end-to-end models reach a WER of 15% which is the lowest
score among all systems.Comment: ICASSP 201
How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language
One of the factors that have hindered progress in the areas of sign language
recognition, translation, and production is the absence of large annotated
datasets. Towards this end, we introduce How2Sign, a multimodal and multiview
continuous American Sign Language (ASL) dataset, consisting of a parallel
corpus of more than 80 hours of sign language videos and a set of corresponding
modalities including speech, English transcripts, and depth. A three-hour
subset was further recorded in the Panoptic studio enabling detailed 3D pose
estimation. To evaluate the potential of How2Sign for real-world impact, we
conduct a study with ASL signers and show that synthesized videos using our
dataset can indeed be understood. The study further gives insights on
challenges that computer vision should address in order to make progress in
this field.
Dataset website: http://how2sign.github.io/Comment: Accepted at CVPR 2021. Dataset website: http://how2sign.github.io
How2Sign: A large-scale multimodal dataset for continuous American sign language
One of the factors that have hindered progress in the areas of sign language recognition, translation, and production is the absence of large annotated datasets. Towards this end, we introduce How2Sign, a multimodal and multiview continuous American Sign Language (ASL) dataset, consisting of a parallel corpus of more than 80 hours of sign language videos and a set of corresponding modalities including speech, English transcripts, and depth. A three-hour subset was further recorded in the Panoptic studio enabling detailed 3D pose estimation. To evaluate the potential of How2Sign for real-world impact, we conduct a study with ASL signers and show that synthesized videos using our dataset can indeed be understood. The study further gives insights on challenges that computer vision should address in order to make progress in this field. Dataset website: http://how2sign.github.io/This work received funding from Facebook through gifts to CMU and UPC; through projects TEC2016-75976-R, TIN2015- 65316-P, SEV-2015-0493 and PID2019-107255GB-C22 of the Spanish Government and 2017-SGR-1414 of Generalitat de Catalunya. This work used XSEDE’s “Bridges” system at the Pittsburgh Supercomputing Center (NSF award ACI- 1445606). Amanda Duarte has received support from la Caixa Foundation (ID 100010434) under the fellowship code LCF/BQ/IN18/11660029. Shruti Palaskar was supported by the Facebook Fellowship program.Peer ReviewedObjectius de Desenvolupament Sostenible::10 - Reducció de les DesigualtatsObjectius de Desenvolupament Sostenible::4 - Educació de Qualitat::4.5 - Per a 2030, eliminar les disparitats de gènere en l’educació i garantir l’accés en condicions d’igualtat a les persones vulnerables, incloses les persones amb discapacitat, els pobles indígenes i els nens i nenes en situacions de vulnerabilitat, a tots els nivells de l’ensenyament i la formació professionalObjectius de Desenvolupament Sostenible::10 - Reducció de les Desigualtats::10.2 - Per a 2030, potenciar i promoure la inclusió social, econòmica i política de totes les persones, independentment de l’edat, sexe, discapacitat, raça, ètnia, origen, religió, situació econòmica o altra condicióObjectius de Desenvolupament Sostenible::4 - Educació de QualitatPostprint (author's final draft