4,383 research outputs found
Articulatory and bottleneck features for speaker-independent ASR of dysarthric speech
The rapid population aging has stimulated the development of assistive
devices that provide personalized medical support to the needies suffering from
various etiologies. One prominent clinical application is a computer-assisted
speech training system which enables personalized speech therapy to patients
impaired by communicative disorders in the patient's home environment. Such a
system relies on the robust automatic speech recognition (ASR) technology to be
able to provide accurate articulation feedback. With the long-term aim of
developing off-the-shelf ASR systems that can be incorporated in clinical
context without prior speaker information, we compare the ASR performance of
speaker-independent bottleneck and articulatory features on dysarthric speech
used in conjunction with dedicated neural network-based acoustic models that
have been shown to be robust against spectrotemporal deviations. We report ASR
performance of these systems on two dysarthric speech datasets of different
characteristics to quantify the achieved performance gains. Despite the
remaining performance gap between the dysarthric and normal speech, significant
improvements have been reported on both datasets using speaker-independent ASR
architectures.Comment: to appear in Computer Speech & Language -
https://doi.org/10.1016/j.csl.2019.05.002 - arXiv admin note: substantial
text overlap with arXiv:1807.1094
DNN adaptation by automatic quality estimation of ASR hypotheses
In this paper we propose to exploit the automatic Quality Estimation (QE) of
ASR hypotheses to perform the unsupervised adaptation of a deep neural network
modeling acoustic probabilities. Our hypothesis is that significant
improvements can be achieved by: i)automatically transcribing the evaluation
data we are currently trying to recognise, and ii) selecting from it a subset
of "good quality" instances based on the word error rate (WER) scores predicted
by a QE component. To validate this hypothesis, we run several experiments on
the evaluation data sets released for the CHiME-3 challenge. First, we operate
in oracle conditions in which manual transcriptions of the evaluation data are
available, thus allowing us to compute the "true" sentence WER. In this
scenario, we perform the adaptation with variable amounts of data, which are
characterised by different levels of quality. Then, we move to realistic
conditions in which the manual transcriptions of the evaluation data are not
available. In this case, the adaptation is performed on data selected according
to the WER scores "predicted" by a QE component. Our results indicate that: i)
QE predictions allow us to closely approximate the adaptation results obtained
in oracle conditions, and ii) the overall ASR performance based on the proposed
QE-driven adaptation method is significantly better than the strong, most
recent, CHiME-3 baseline.Comment: Computer Speech & Language December 201
Interactive neural machine translation
This is the author’s version of a work that was accepted for publication in Computer Speech & Language. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Speech & Language 00 (2016) 1 20. DOI 10.1016/j.csl.2016.12.003.Despite the promising results achieved in last years by statistical machine translation, and more precisely, by the neural
machine translation systems, this technology is still not error-free. The outputs of a machine translation system must be corrected
by a human agent in a post-editing phase. Interactive protocols foster a human computer collaboration, in order to increase productivity.
In this work, we integrate the neural machine translation into the interactive machine translation framework. Moreover,
we propose new interactivity protocols, in order to provide the user an enhanced experience and a higher productivity. Results
obtained over a simulated benchmark show that interactive neural systems can significantly improve the classical phrase-based
approach in an interactive-predictive machine translation scenario.
c 2016 Elsevier Ltd. All rights reserved.The authors wish to thank the anonymous reviewers for their careful reading and in-depth criticisms and suggestions. This work was partially funded by the project ALMAMATER (PrometeoII/2014/030). We also acknowledge NVIDIA for the donation of the GPU used in this work.Peris Abril, Á.; Domingo-Ballester, M.; Casacuberta Nolla, F. (2017). Interactive neural machine translation. Computer Speech and Language. 1-20. https://doi.org/10.1016/j.csl.2016.12.003S12
Latent Tree Language Model
In this paper we introduce Latent Tree Language Model (LTLM), a novel
approach to language modeling that encodes syntax and semantics of a given
sentence as a tree of word roles.
The learning phase iteratively updates the trees by moving nodes according to
Gibbs sampling. We introduce two algorithms to infer a tree for a given
sentence. The first one is based on Gibbs sampling. It is fast, but does not
guarantee to find the most probable tree. The second one is based on dynamic
programming. It is slower, but guarantees to find the most probable tree. We
provide comparison of both algorithms.
We combine LTLM with 4-gram Modified Kneser-Ney language model via linear
interpolation. Our experiments with English and Czech corpora show significant
perplexity reductions (up to 46% for English and 49% for Czech) compared with
standalone 4-gram Modified Kneser-Ney language model.Comment: Accepted to EMNLP 201
Optimizing expected word error rate via sampling for speech recognition
State-level minimum Bayes risk (sMBR) training has become the de facto
standard for sequence-level training of speech recognition acoustic models. It
has an elegant formulation using the expectation semiring, and gives large
improvements in word error rate (WER) over models trained solely using
cross-entropy (CE) or connectionist temporal classification (CTC). sMBR
training optimizes the expected number of frames at which the reference and
hypothesized acoustic states differ. It may be preferable to optimize the
expected WER, but WER does not interact well with the expectation semiring, and
previous approaches based on computing expected WER exactly involve expanding
the lattices used during training. In this paper we show how to perform
optimization of the expected WER by sampling paths from the lattices used
during conventional sMBR training. The gradient of the expected WER is itself
an expectation, and so may be approximated using Monte Carlo sampling. We show
experimentally that optimizing WER during acoustic model training gives 5%
relative improvement in WER over a well-tuned sMBR baseline on a 2-channel
query recognition task (Google Home)
Large Margin Neural Language Model
We propose a large margin criterion for training neural language models.
Conventionally, neural language models are trained by minimizing perplexity
(PPL) on grammatical sentences. However, we demonstrate that PPL may not be the
best metric to optimize in some tasks, and further propose a large margin
formulation. The proposed method aims to enlarge the margin between the "good"
and "bad" sentences in a task-specific sense. It is trained end-to-end and can
be widely applied to tasks that involve re-scoring of generated text. Compared
with minimum-PPL training, our method gains up to 1.1 WER reduction for speech
recognition and 1.0 BLEU increase for machine translation.Comment: 9 pages. Accepted as a long paper in EMNLP201
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