Learning weakly supervised multimodal phoneme embeddings

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

Language Identification Using Visual Features

Automatic visual language identification (VLID) is the technology of using information derived from the visual appearance and movement of the speech articulators to iden- tify the language being spoken, without the use of any audio information. This technique for language identification (LID) is useful in situations in which conventional audio processing is ineffective (very noisy environments), or impossible (no audio signal is available). Research in this field is also beneficial in the related field of automatic lip-reading. This paper introduces several methods for visual language identification (VLID). They are based upon audio LID techniques, which exploit language phonology and phonotactics to discriminate languages. We show that VLID is possible in a speaker-dependent mode by discrimi- nating different languages spoken by an individual, and we then extend the technique to speaker-independent operation, taking pains to ensure that discrimination is not due to artefacts, either visual (e.g. skin-tone) or audio (e.g. rate of speaking). Although the low accuracy of visual speech recognition currently limits the performance of VLID, we can obtain an error-rate of < 10% in discriminating between Arabic and English on 19 speakers and using about 30s of visual speech

Harnessing AI for Speech Reconstruction using Multi-view Silent Video Feed

Speechreading or lipreading is the technique of understanding and getting phonetic features from a speaker's visual features such as movement of lips, face, teeth and tongue. It has a wide range of multimedia applications such as in surveillance, Internet telephony, and as an aid to a person with hearing impairments. However, most of the work in speechreading has been limited to text generation from silent videos. Recently, research has started venturing into generating (audio) speech from silent video sequences but there have been no developments thus far in dealing with divergent views and poses of a speaker. Thus although, we have multiple camera feeds for the speech of a user, but we have failed in using these multiple video feeds for dealing with the different poses. To this end, this paper presents the world's first ever multi-view speech reading and reconstruction system. This work encompasses the boundaries of multimedia research by putting forth a model which leverages silent video feeds from multiple cameras recording the same subject to generate intelligent speech for a speaker. Initial results confirm the usefulness of exploiting multiple camera views in building an efficient speech reading and reconstruction system. It further shows the optimal placement of cameras which would lead to the maximum intelligibility of speech. Next, it lays out various innovative applications for the proposed system focusing on its potential prodigious impact in not just security arena but in many other multimedia analytics problems.Comment: 2018 ACM Multimedia Conference (MM '18), October 22--26, 2018, Seoul, Republic of Kore

Visual units and confusion modelling for automatic lip-reading

Automatic lip-reading (ALR) is a challenging task because the visual speech signal is known to be missing some important information, such as voicing. We propose an approach to ALR that acknowledges that this information is missing but assumes that it is substituted or deleted in a systematic way that can be modelled. We describe a system that learns such a model and then incorporates it into decoding, which is realised as a cascade of weighted finite-state transducers. Our results show a small but statistically significant improvement in recognition accuracy. We also investigate the issue of suitable visual units for ALR, and show that visemes are sub-optimal, not but because they introduce lexical ambiguity, but because the reduction in modelling units entailed by their use reduces accuracy