ISCA Archive Interspeech 2017
ISCA Archive Interspeech 2017

Learning Weakly Supervised Multimodal Phoneme Embeddings

Rahma Chaabouni, Ewan Dunbar, Neil Zeghidour, Emmanuel Dupoux

Recent works have explored deep architectures for learning multimodal speech representation (e.g. audio and images, articulation and audio) in a supervised way. Here we investigate the role of combining different speech modalities, i.e. audio and visual information representing the lips’ movements, in a weakly supervised way using Siamese networks and lexical same-different side information. In particular, we ask whether one modality can benefit from the other to provide a richer representation for phone recognition in a weakly supervised setting. We introduce mono-task and multi-task methods for merging speech and visual modalities for phone recognition. The mono-task learning consists in applying a Siamese network on the concatenation of the two modalities, while the multi-task learning receives several different combinations of modalities at train time. We show that multi-task learning enhances discriminability for visual and multimodal inputs while minimally impacting auditory inputs. Furthermore, we present a qualitative analysis of the obtained phone embeddings, and show that cross-modal visual input can improve the discriminability of phonological features which are visually discernable (rounding, open/close, labial place of articulation), resulting in representations that are closer to abstract linguistic features than those based on audio only.

doi: 10.21437/Interspeech.2017-1689

Cite as: Chaabouni, R., Dunbar, E., Zeghidour, N., Dupoux, E. (2017) Learning Weakly Supervised Multimodal Phoneme Embeddings. Proc. Interspeech 2017, 2218-2222, doi: 10.21437/Interspeech.2017-1689

  author={Rahma Chaabouni and Ewan Dunbar and Neil Zeghidour and Emmanuel Dupoux},
  title={{Learning Weakly Supervised Multimodal Phoneme Embeddings}},
  booktitle={Proc. Interspeech 2017},