74 research outputs found
Combining Residual Networks with LSTMs for Lipreading
We propose an end-to-end deep learning architecture for word-level visual
speech recognition. The system is a combination of spatiotemporal
convolutional, residual and bidirectional Long Short-Term Memory networks. We
train and evaluate it on the Lipreading In-The-Wild benchmark, a challenging
database of 500-size target-words consisting of 1.28sec video excerpts from BBC
TV broadcasts. The proposed network attains word accuracy equal to 83.0,
yielding 6.8 absolute improvement over the current state-of-the-art, without
using information about word boundaries during training or testing.Comment: Submitted to Interspeech 201
Investigation of Frame Alignments for GMM-based Digit-prompted Speaker Verification
Frame alignments can be computed by different methods in GMM-based speaker
verification. By incorporating a phonetic Gaussian mixture model (PGMM), we are
able to compare the performance using alignments extracted from the deep neural
networks (DNN) and the conventional hidden Markov model (HMM) in digit-prompted
speaker verification. Based on the different characteristics of these two
alignments, we present a novel content verification method to improve the
system security without much computational overhead. Our experiments on the
RSR2015 Part-3 digit-prompted task show that, the DNN based alignment performs
on par with the HMM alignment. The results also demonstrate the effectiveness
of the proposed Kullback-Leibler (KL) divergence based scoring to reject speech
with incorrect pass-phrases.Comment: accepted by APSIPA ASC 201
Combining residual networks with LSTMs for lipreading
We propose an end-to-end deep learning architecture for word level visual speech recognition. The system is a combination of spatiotemporal convolutional, residual and bidirectional Long Short-Term Memory networks. We trained and evaluated it on the Lipreading In-The-Wild benchmark, a challenging database of 500-size vocabulary consisting of video excerpts from BBC TV broadcasts. The proposed network attains word accuracy equal to 83.0%, yielding 6.8% absolute improvement over the current state-of-the-art
Deep word embeddings for visual speech recognition
In this paper we present a deep learning architecture for extracting word embeddings for visual speech recognition. The embeddings summarize the information of the mouth region that is relevant to the problem of word recognition, while suppressing other types of variability such as speaker, pose and illumination. The system is comprised of a spatiotemporal convolutional layer, a Residual Network and bidirectional LSTMs and is trained on the Lipreading in-the-wild database. We first show that the proposed architecture goes beyond state-of-the-art on closed-set word identification, by attaining 11.92% error rate on a vocabulary of 500 words. We then examine the capacity of the embeddings in modelling words unseen during training. We deploy Probabilistic Linear Discriminant Analysis (PLDA) to model the embeddings and perform low-shot learning experiments on words unseen during training. The experiments demonstrate that word-level visual speech recognition is feasible even in cases where the target words are not included in the training set
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