18,999 research outputs found
Real-Time Statistical Speech Translation
This research investigates the Statistical Machine Translation approaches to
translate speech in real time automatically. Such systems can be used in a
pipeline with speech recognition and synthesis software in order to produce a
real-time voice communication system between foreigners. We obtained three main
data sets from spoken proceedings that represent three different types of human
speech. TED, Europarl, and OPUS parallel text corpora were used as the basis
for training of language models, for developmental tuning and testing of the
translation system. We also conducted experiments involving part of speech
tagging, compound splitting, linear language model interpolation, TrueCasing
and morphosyntactic analysis. We evaluated the effects of variety of data
preparations on the translation results using the BLEU, NIST, METEOR and TER
metrics and tried to give answer which metric is most suitable for PL-EN
language pair.Comment: machine translation, polish englis
Using same-language machine translation to create alternative target sequences for text-to-speech synthesis
Modern speech synthesis systems attempt to produce
speech utterances from an open domain of words. In some situations, the synthesiser will not have the appropriate units to pronounce some words or phrases accurately but it still must attempt to pronounce them. This paper presents a hybrid machine translation and unit selection speech synthesis system. The machine translation system was trained with English as the source and target language. Rather than the synthesiser only saying the input text as would happen in conventional synthesis systems, the synthesiser may say an alternative utterance with the same
meaning. This method allows the synthesiser to overcome the
problem of insufficient units in runtime
Supervised and Unsupervised Transfer Learning for Question Answering
Although transfer learning has been shown to be successful for tasks like
object and speech recognition, its applicability to question answering (QA) has
yet to be well-studied. In this paper, we conduct extensive experiments to
investigate the transferability of knowledge learned from a source QA dataset
to a target dataset using two QA models. The performance of both models on a
TOEFL listening comprehension test (Tseng et al., 2016) and MCTest (Richardson
et al., 2013) is significantly improved via a simple transfer learning
technique from MovieQA (Tapaswi et al., 2016). In particular, one of the models
achieves the state-of-the-art on all target datasets; for the TOEFL listening
comprehension test, it outperforms the previous best model by 7%. Finally, we
show that transfer learning is helpful even in unsupervised scenarios when
correct answers for target QA dataset examples are not available.Comment: To appear in NAACL HLT 2018 (long paper
Estimating Performance of Pipelined Spoken Language Translation Systems
Most spoken language translation systems developed to date rely on a
pipelined architecture, in which the main stages are speech recognition,
linguistic analysis, transfer, generation and speech synthesis. When making
projections of error rates for systems of this kind, it is natural to assume
that the error rates for the individual components are independent, making the
system accuracy the product of the component accuracies.
The paper reports experiments carried out using the SRI-SICS-Telia Research
Spoken Language Translator and a 1000-utterance sample of unseen data. The
results suggest that the naive performance model leads to serious overestimates
of system error rates, since there are in fact strong dependencies between the
components. Predicting the system error rate on the independence assumption by
simple multiplication resulted in a 16\% proportional overestimate for all
utterances, and a 19\% overestimate when only utterances of length 1-10 words
were considered.Comment: 10 pages, Latex source. To appear in Proc. ICSLP '9
Transfer Learning for Speech and Language Processing
Transfer learning is a vital technique that generalizes models trained for
one setting or task to other settings or tasks. For example in speech
recognition, an acoustic model trained for one language can be used to
recognize speech in another language, with little or no re-training data.
Transfer learning is closely related to multi-task learning (cross-lingual vs.
multilingual), and is traditionally studied in the name of `model adaptation'.
Recent advance in deep learning shows that transfer learning becomes much
easier and more effective with high-level abstract features learned by deep
models, and the `transfer' can be conducted not only between data distributions
and data types, but also between model structures (e.g., shallow nets and deep
nets) or even model types (e.g., Bayesian models and neural models). This
review paper summarizes some recent prominent research towards this direction,
particularly for speech and language processing. We also report some results
from our group and highlight the potential of this very interesting research
field.Comment: 13 pages, APSIPA 201
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