1,689 research outputs found
Text Preprocessing for Speech Synthesis
In this paper we describe our text preprocessing modules for English text-to-speech synthesis. These modules comprise rule-based text normalization subsuming sentence segmentation and normalization of non-standard words, statistical part-of-speech tagging, and statistical syllabification, grapheme-to-phoneme conversion, and word stress assignment relying in parts on rule-based morphological analysis
A Comparison of Different Machine Transliteration Models
Machine transliteration is a method for automatically converting words in one
language into phonetically equivalent ones in another language. Machine
transliteration plays an important role in natural language applications such
as information retrieval and machine translation, especially for handling
proper nouns and technical terms. Four machine transliteration models --
grapheme-based transliteration model, phoneme-based transliteration model,
hybrid transliteration model, and correspondence-based transliteration model --
have been proposed by several researchers. To date, however, there has been
little research on a framework in which multiple transliteration models can
operate simultaneously. Furthermore, there has been no comparison of the four
models within the same framework and using the same data. We addressed these
problems by 1) modeling the four models within the same framework, 2) comparing
them under the same conditions, and 3) developing a way to improve machine
transliteration through this comparison. Our comparison showed that the hybrid
and correspondence-based models were the most effective and that the four
models can be used in a complementary manner to improve machine transliteration
performance
Few-Shot and Zero-Shot Learning for Historical Text Normalization
Historical text normalization often relies on small training datasets. Recent
work has shown that multi-task learning can lead to significant improvements by
exploiting synergies with related datasets, but there has been no systematic
study of different multi-task learning architectures. This paper evaluates
63~multi-task learning configurations for sequence-to-sequence-based historical
text normalization across ten datasets from eight languages, using
autoencoding, grapheme-to-phoneme mapping, and lemmatization as auxiliary
tasks. We observe consistent, significant improvements across languages when
training data for the target task is limited, but minimal or no improvements
when training data is abundant. We also show that zero-shot learning
outperforms the simple, but relatively strong, identity baseline.Comment: Accepted at DeepLo-201
Zero-shot keyword spotting for visual speech recognition in-the-wild
Visual keyword spotting (KWS) is the problem of estimating whether a text
query occurs in a given recording using only video information. This paper
focuses on visual KWS for words unseen during training, a real-world, practical
setting which so far has received no attention by the community. To this end,
we devise an end-to-end architecture comprising (a) a state-of-the-art visual
feature extractor based on spatiotemporal Residual Networks, (b) a
grapheme-to-phoneme model based on sequence-to-sequence neural networks, and
(c) a stack of recurrent neural networks which learn how to correlate visual
features with the keyword representation. Different to prior works on KWS,
which try to learn word representations merely from sequences of graphemes
(i.e. letters), we propose the use of a grapheme-to-phoneme encoder-decoder
model which learns how to map words to their pronunciation. We demonstrate that
our system obtains very promising visual-only KWS results on the challenging
LRS2 database, for keywords unseen during training. We also show that our
system outperforms a baseline which addresses KWS via automatic speech
recognition (ASR), while it drastically improves over other recently proposed
ASR-free KWS methods.Comment: Accepted at ECCV-201
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