1,278 research outputs found
Design of a Controlled Language for Critical Infrastructures Protection
We describe a project for the construction of controlled language for critical infrastructures protection (CIP). This project originates
from the need to coordinate and categorize the communications on CIP at the European level. These communications can be physically
represented by official documents, reports on incidents, informal communications and plain e-mail. We explore the application of
traditional library science tools for the construction of controlled languages in order to achieve our goal. Our starting point is an
analogous work done during the sixties in the field of nuclear science known as the Euratom Thesaurus.JRC.G.6-Security technology assessmen
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Deep Learning for Automatic Assessment and Feedback of Spoken English
Growing global demand for learning a second language (L2), particularly English, has led to
considerable interest in automatic spoken language assessment, whether for use in computerassisted language learning (CALL) tools or for grading candidates for formal qualifications.
This thesis presents research conducted into the automatic assessment of spontaneous nonnative English speech, with a view to be able to provide meaningful feedback to learners. One
of the challenges in automatic spoken language assessment is giving candidates feedback on
particular aspects, or views, of their spoken language proficiency, in addition to the overall
holistic score normally provided. Another is detecting pronunciation and other types of errors
at the word or utterance level and feeding them back to the learner in a useful way.
It is usually difficult to obtain accurate training data with separate scores for different
views and, as examiners are often trained to give holistic grades, single-view scores can
suffer issues of consistency. Conversely, holistic scores are available for various standard
assessment tasks such as Linguaskill. An investigation is thus conducted into whether
assessment scores linked to particular views of the speaker’s ability can be obtained from
systems trained using only holistic scores.
End-to-end neural systems are designed with structures and forms of input tuned to single
views, specifically each of pronunciation, rhythm, intonation and text. By training each
system on large quantities of candidate data, individual-view information should be possible
to extract. The relationships between the predictions of each system are evaluated to examine
whether they are, in fact, extracting different information about the speaker. Three methods
of combining the systems to predict holistic score are investigated, namely averaging their
predictions and concatenating and attending over their intermediate representations. The
combined graders are compared to each other and to baseline approaches.
The tasks of error detection and error tendency diagnosis become particularly challenging
when the speech in question is spontaneous and particularly given the challenges posed by
the inconsistency of human annotation of pronunciation errors. An approach to these tasks is
presented by distinguishing between lexical errors, wherein the speaker does not know how a
particular word is pronounced, and accent errors, wherein the candidate’s speech exhibits
consistent patterns of phone substitution, deletion and insertion. Three annotated corpora
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of non-native English speech by speakers of multiple L1s are analysed, the consistency of
human annotation investigated and a method presented for detecting individual accent and
lexical errors and diagnosing accent error tendencies at the speaker level
Dimensions of Segmental Variability: Interaction of Prosody and Surprisal in Six Languages
Contextual predictability variation affects phonological and phonetic structure. Reduction and expansion of acoustic-phonetic features is also characteristic of prosodic variability. In this study, we assess the impact of surprisal and prosodic structure on phonetic encoding, both independently of each other and in interaction. We model segmental duration, vowel space size and spectral characteristics of vowels and consonants as a function of surprisal as well as of syllable prominence, phrase boundary, and speech rate. Correlates of phonetic encoding density are extracted from a subset of the BonnTempo corpus for six languages: American English, Czech, Finnish, French, German, and Polish. Surprisal is estimated from segmental n-gram language models trained on large text corpora. Our findings are generally compatible with a weak version of Aylett and Turk's Smooth Signal Redundancy hypothesis, suggesting that prosodic structure mediates between the requirements of efficient communication and the speech signal. However, this mediation is not perfect, as we found evidence for additional, direct effects of changes in surprisal on the phonetic structure of utterances. These effects appear to be stable across different speech rates
VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation
We introduce VoxPopuli, a large-scale multilingual corpus providing 100K
hours of unlabelled speech data in 23 languages. It is the largest open data to
date for unsupervised representation learning as well as semi-supervised
learning. VoxPopuli also contains 1.8K hours of transcribed speeches in 16
languages and their aligned oral interpretations into 5 other languages
totaling 5.1K hours. We provide speech recognition baselines and validate the
versatility of VoxPopuli unlabelled data in semi-supervised learning under
challenging out-of-domain settings. We will release the corpus at
https://github.com/facebookresearch/voxpopuli under an open license.Comment: Accepted to ACL 2021 (long paper
Spoken content retrieval: A survey of techniques and technologies
Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR
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