8,812 research outputs found
Internet delivery of time-synchronised multimedia: the SCOTS projects
The Scottish Corpus of Texts and Speech (SCOTS) Project at Glasgow University aims to make available over the Internet a 4 million-word multimedia corpus of texts in the languages of Scotland. Twenty percent of this final total will comprise spoken language, in a combination of audio and video material. Versions of SCOTS have been accessible on the Internet since November 2004, and regular additions are made to the Corpus as texts are processed and functionality is improved. While the Corpus is a valuable resource for research, our target users also include the general public, and this has important implications for the nature of the Corpus and website.
This paper will begin with a general introduction to the SCOTS Project, and in particular to the nature of our data. The main part of the paper will then present the approach taken to spoken texts. Transcriptions are made using Praat (Boersma and Weenink, University of Amsterdam), which produces a time-based transcription and allows for multiple speakers though independent tiers. This output is then processed to produce a turn-based transcription with overlap and non-linguistic noises indicated. As this transcription is synchronised with the source audio/video material it allows users direct access to any particular passage of the recording, possibly based upon a word query. This process and the end result will be demonstrated and discussed.
We shall end by considering the value which is added to an Internet-delivered Corpus by these means of treating spoken text. The advantages include the possibility of returning search results from both written texts and multimedia documents; the easy location of the relevant section of the audio file; and the production through Praat of a turn-based orthographic transcription, which is accessible to a general as well as an academic user. These techniques can also be extended to other research requirements, such as the mark-up of gesture in video texts
A Formal Framework for Linguistic Annotation
`Linguistic annotation' covers any descriptive or analytic notations applied
to raw language data. The basic data may be in the form of time functions --
audio, video and/or physiological recordings -- or it may be textual. The added
notations may include transcriptions of all sorts (from phonetic features to
discourse structures), part-of-speech and sense tagging, syntactic analysis,
`named entity' identification, co-reference annotation, and so on. While there
are several ongoing efforts to provide formats and tools for such annotations
and to publish annotated linguistic databases, the lack of widely accepted
standards is becoming a critical problem. Proposed standards, to the extent
they exist, have focussed on file formats. This paper focuses instead on the
logical structure of linguistic annotations. We survey a wide variety of
existing annotation formats and demonstrate a common conceptual core, the
annotation graph. This provides a formal framework for constructing,
maintaining and searching linguistic annotations, while remaining consistent
with many alternative data structures and file formats.Comment: 49 page
Radio Oranje: Enhanced Access to a Historical Spoken Word Collection
Access to historical audio collections is typically very restricted:\ud
content is often only available on physical (analog) media and the\ud
metadata is usually limited to keywords, giving access at the level\ud
of relatively large fragments, e.g., an entire tape. Many spoken\ud
word heritage collections are now being digitized, which allows the\ud
introduction of more advanced search technology. This paper presents\ud
an approach that supports online access and search for recordings of\ud
historical speeches. A demonstrator has been built, based on the\ud
so-called Radio Oranje collection, which contains radio speeches by\ud
the Dutch Queen Wilhelmina that were broadcast during World War II.\ud
The audio has been aligned with its original 1940s manual\ud
transcriptions to create a time-stamped index that enables the speeches to be\ud
searched at the word level. Results are presented together with\ud
related photos from an external database
Learning weakly supervised multimodal phoneme embeddings
Recent works have explored deep architectures for learning multimodal speech
representation (e.g. audio and images, articulation and audio) in a supervised
way. Here we investigate the role of combining different speech modalities,
i.e. audio and visual information representing the lips movements, in a weakly
supervised way using Siamese networks and lexical same-different side
information. In particular, we ask whether one modality can benefit from the
other to provide a richer representation for phone recognition in a weakly
supervised setting. We introduce mono-task and multi-task methods for merging
speech and visual modalities for phone recognition. The mono-task learning
consists in applying a Siamese network on the concatenation of the two
modalities, while the multi-task learning receives several different
combinations of modalities at train time. We show that multi-task learning
enhances discriminability for visual and multimodal inputs while minimally
impacting auditory inputs. Furthermore, we present a qualitative analysis of
the obtained phone embeddings, and show that cross-modal visual input can
improve the discriminability of phonological features which are visually
discernable (rounding, open/close, labial place of articulation), resulting in
representations that are closer to abstract linguistic features than those
based on audio only
BEA – A multifunctional Hungarian spoken language database
In diverse areas of linguistics, the demand for studying actual language use is on
the increase. The aim of developing a phonetically-based multi-purpose database of
Hungarian spontaneous speech, dubbed BEA2, is to accumulate a large amount of
spontaneous speech of various types together with sentence repetition and reading.
Presently, the recorded material of BEA amounts to 260 hours produced by 280
present-day Budapest speakers (ages between 20 and 90, 168 females and 112
males), providing also annotated materials for various types of research and practical
applications
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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