4,024 research outputs found
Disentangled Speech Embeddings using Cross-modal Self-supervision
The objective of this paper is to learn representations of speaker identity
without access to manually annotated data. To do so, we develop a
self-supervised learning objective that exploits the natural cross-modal
synchrony between faces and audio in video. The key idea behind our approach is
to tease apart--without annotation--the representations of linguistic content
and speaker identity. We construct a two-stream architecture which: (1) shares
low-level features common to both representations; and (2) provides a natural
mechanism for explicitly disentangling these factors, offering the potential
for greater generalisation to novel combinations of content and identity and
ultimately producing speaker identity representations that are more robust. We
train our method on a large-scale audio-visual dataset of talking heads `in the
wild', and demonstrate its efficacy by evaluating the learned speaker
representations for standard speaker recognition performance.Comment: ICASSP 2020. The first three authors contributed equally to this wor
Learnable PINs: Cross-Modal Embeddings for Person Identity
We propose and investigate an identity sensitive joint embedding of face and
voice. Such an embedding enables cross-modal retrieval from voice to face and
from face to voice. We make the following four contributions: first, we show
that the embedding can be learnt from videos of talking faces, without
requiring any identity labels, using a form of cross-modal self-supervision;
second, we develop a curriculum learning schedule for hard negative mining
targeted to this task, that is essential for learning to proceed successfully;
third, we demonstrate and evaluate cross-modal retrieval for identities unseen
and unheard during training over a number of scenarios and establish a
benchmark for this novel task; finally, we show an application of using the
joint embedding for automatically retrieving and labelling characters in TV
dramas.Comment: To appear in ECCV 201
Semi-supervised Multi-modal Emotion Recognition with Cross-Modal Distribution Matching
Automatic emotion recognition is an active research topic with wide range of
applications. Due to the high manual annotation cost and inevitable label
ambiguity, the development of emotion recognition dataset is limited in both
scale and quality. Therefore, one of the key challenges is how to build
effective models with limited data resource. Previous works have explored
different approaches to tackle this challenge including data enhancement,
transfer learning, and semi-supervised learning etc. However, the weakness of
these existing approaches includes such as training instability, large
performance loss during transfer, or marginal improvement.
In this work, we propose a novel semi-supervised multi-modal emotion
recognition model based on cross-modality distribution matching, which
leverages abundant unlabeled data to enhance the model training under the
assumption that the inner emotional status is consistent at the utterance level
across modalities.
We conduct extensive experiments to evaluate the proposed model on two
benchmark datasets, IEMOCAP and MELD. The experiment results prove that the
proposed semi-supervised learning model can effectively utilize unlabeled data
and combine multi-modalities to boost the emotion recognition performance,
which outperforms other state-of-the-art approaches under the same condition.
The proposed model also achieves competitive capacity compared with existing
approaches which take advantage of additional auxiliary information such as
speaker and interaction context.Comment: 10 pages, 5 figures, to be published on ACM Multimedia 202
Bio-Inspired Modality Fusion for Active Speaker Detection
Human beings have developed fantastic abilities to integrate information from
various sensory sources exploring their inherent complementarity. Perceptual
capabilities are therefore heightened enabling, for instance, the well known
"cocktail party" and McGurk effects, i.e. speech disambiguation from a panoply
of sound signals. This fusion ability is also key in refining the perception of
sound source location, as in distinguishing whose voice is being heard in a
group conversation. Furthermore, Neuroscience has successfully identified the
superior colliculus region in the brain as the one responsible for this
modality fusion, with a handful of biological models having been proposed to
approach its underlying neurophysiological process. Deriving inspiration from
one of these models, this paper presents a methodology for effectively fusing
correlated auditory and visual information for active speaker detection. Such
an ability can have a wide range of applications, from teleconferencing systems
to social robotics. The detection approach initially routes auditory and visual
information through two specialized neural network structures. The resulting
embeddings are fused via a novel layer based on the superior colliculus, whose
topological structure emulates spatial neuron cross-mapping of unimodal
perceptual fields. The validation process employed two publicly available
datasets, with achieved results confirming and greatly surpassing initial
expectations.Comment: Submitted to IEEE RA-L with IROS option, 202
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