2,520 research outputs found
Recurrent DNNs and its Ensembles on the TIMIT Phone Recognition Task
In this paper, we have investigated recurrent deep neural networks (DNNs) in
combination with regularization techniques as dropout, zoneout, and
regularization post-layer. As a benchmark, we chose the TIMIT phone recognition
task due to its popularity and broad availability in the community. It also
simulates a low-resource scenario that is helpful in minor languages. Also, we
prefer the phone recognition task because it is much more sensitive to an
acoustic model quality than a large vocabulary continuous speech recognition
task. In recent years, recurrent DNNs pushed the error rates in automatic
speech recognition down. But, there was no clear winner in proposed
architectures. The dropout was used as the regularization technique in most
cases, but combination with other regularization techniques together with model
ensembles was omitted. However, just an ensemble of recurrent DNNs performed
best and achieved an average phone error rate from 10 experiments 14.84 %
(minimum 14.69 %) on core test set that is slightly lower then the
best-published PER to date, according to our knowledge. Finally, in contrast of
the most papers, we published the open-source scripts to easily replicate the
results and to help continue the development.Comment: Submitted to SPECOM 2018, 20th International Conference on Speech and
Compute
Unconstrained Scene Text and Video Text Recognition for Arabic Script
Building robust recognizers for Arabic has always been challenging. We
demonstrate the effectiveness of an end-to-end trainable CNN-RNN hybrid
architecture in recognizing Arabic text in videos and natural scenes. We
outperform previous state-of-the-art on two publicly available video text
datasets - ALIF and ACTIV. For the scene text recognition task, we introduce a
new Arabic scene text dataset and establish baseline results. For scripts like
Arabic, a major challenge in developing robust recognizers is the lack of large
quantity of annotated data. We overcome this by synthesising millions of Arabic
text images from a large vocabulary of Arabic words and phrases. Our
implementation is built on top of the model introduced here [37] which is
proven quite effective for English scene text recognition. The model follows a
segmentation-free, sequence to sequence transcription approach. The network
transcribes a sequence of convolutional features from the input image to a
sequence of target labels. This does away with the need for segmenting input
image into constituent characters/glyphs, which is often difficult for Arabic
script. Further, the ability of RNNs to model contextual dependencies yields
superior recognition results.Comment: 5 page
Not All Dialogues are Created Equal: Instance Weighting for Neural Conversational Models
Neural conversational models require substantial amounts of dialogue data for
their parameter estimation and are therefore usually learned on large corpora
such as chat forums or movie subtitles. These corpora are, however, often
challenging to work with, notably due to their frequent lack of turn
segmentation and the presence of multiple references external to the dialogue
itself. This paper shows that these challenges can be mitigated by adding a
weighting model into the architecture. The weighting model, which is itself
estimated from dialogue data, associates each training example to a numerical
weight that reflects its intrinsic quality for dialogue modelling. At training
time, these sample weights are included into the empirical loss to be
minimised. Evaluation results on retrieval-based models trained on movie and TV
subtitles demonstrate that the inclusion of such a weighting model improves the
model performance on unsupervised metrics.Comment: Accepted to SIGDIAL 201
- …