70,983 research outputs found
Efficient Document Re-Ranking for Transformers by Precomputing Term Representations
Deep pretrained transformer networks are effective at various ranking tasks,
such as question answering and ad-hoc document ranking. However, their
computational expenses deem them cost-prohibitive in practice. Our proposed
approach, called PreTTR (Precomputing Transformer Term Representations),
considerably reduces the query-time latency of deep transformer networks (up to
a 42x speedup on web document ranking) making these networks more practical to
use in a real-time ranking scenario. Specifically, we precompute part of the
document term representations at indexing time (without a query), and merge
them with the query representation at query time to compute the final ranking
score. Due to the large size of the token representations, we also propose an
effective approach to reduce the storage requirement by training a compression
layer to match attention scores. Our compression technique reduces the storage
required up to 95% and it can be applied without a substantial degradation in
ranking performance.Comment: Accepted at SIGIR 2020 (long
End-to-end Audiovisual Speech Activity Detection with Bimodal Recurrent Neural Models
Speech activity detection (SAD) plays an important role in current speech
processing systems, including automatic speech recognition (ASR). SAD is
particularly difficult in environments with acoustic noise. A practical
solution is to incorporate visual information, increasing the robustness of the
SAD approach. An audiovisual system has the advantage of being robust to
different speech modes (e.g., whisper speech) or background noise. Recent
advances in audiovisual speech processing using deep learning have opened
opportunities to capture in a principled way the temporal relationships between
acoustic and visual features. This study explores this idea proposing a
\emph{bimodal recurrent neural network} (BRNN) framework for SAD. The approach
models the temporal dynamic of the sequential audiovisual data, improving the
accuracy and robustness of the proposed SAD system. Instead of estimating
hand-crafted features, the study investigates an end-to-end training approach,
where acoustic and visual features are directly learned from the raw data
during training. The experimental evaluation considers a large audiovisual
corpus with over 60.8 hours of recordings, collected from 105 speakers. The
results demonstrate that the proposed framework leads to absolute improvements
up to 1.2% under practical scenarios over a VAD baseline using only audio
implemented with deep neural network (DNN). The proposed approach achieves
92.7% F1-score when it is evaluated using the sensors from a portable tablet
under noisy acoustic environment, which is only 1.0% lower than the performance
obtained under ideal conditions (e.g., clean speech obtained with a high
definition camera and a close-talking microphone).Comment: Submitted to Speech Communicatio
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