13,740 research outputs found
BEA â 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
Computer-assisted transcription and analysis of speech
The two papers included in this volume have developed from work with the CHILDES tools and the Media Editor in the two research projects, "Second language acquisition of German by Russian learners", sponsored by the Max Planck Institute for Psycholinguistics, Nijmegen, from 1998 to 1999 (directed by Ursula Stephany, University of Cologne, and Wolfgang Klein, Max Planck Institute for Psycholinguistics, Nijmegen) and "The age factor in the acquisition of German as a second language", sponsored by the German Science Foundation (DFG), Bonn, since 2000 (directed by Ursula Stephany, University of Cologne, and Christine Dimroth, Max Planck Institute for Psycholinguistics, Nijmegen). The CHILDES Project has been developed and is being continuously improved at Carnegie Mellon University, Pittsburgh, under the supervision of Brian MacWhinney. Having used the CHILDES tools for more than ten years for transcribing and analyzing Greek child data there it was no question that I would also use them for research into the acquisition of German as a second language and analyze the big amount of spontaneous speech gathered from two Russian girls with the help of the CLAN programs. When in the spring of 1997, Steven Gillis from the University of Antwerp (in collaboration with Gert Durieux) developed a lexicon-based automatic coding system based on the CLAN program MOR and suitable for coding languages with richer morphologies than English, such as Modern Greek. Coding huge amounts of data then became much quicker and more comfortable so that I decided to adopt this system for German as well. The paper "Working with the CHILDES Tools" is based on two earlier manuscripts which have grown out of my research on Greek child language and the many CHILDES workshops taught in Germany, Greece, Portugal, and Brazil over the years. Its contents have now been adapted to the requirements of research into the acquisition of German as a second language and for use on Windows
Robust audio indexing for Dutch spoken-word collections
AbstractâWhereas the growth of storage capacity is in accordance with widely acknowledged predictions, the possibilities to index and access the archives created is lagging behind. This is especially the case in the oral history domain and much of the rich content in these collections runs the risk to remain inaccessible for lack of robust search technologies. This paper addresses the history and development of robust audio indexing technology for searching Dutch spoken-word collections and compares Dutch audio indexing in the well-studied broadcast news domain with an oral-history case-study. It is concluded that despite significant advances in Dutch audio indexing technology and demonstrated applicability in several domains, further research is indispensable for successful automatic disclosure of spoken-word collections
Object Referring in Visual Scene with Spoken Language
Object referring has important applications, especially for human-machine
interaction. While having received great attention, the task is mainly attacked
with written language (text) as input rather than spoken language (speech),
which is more natural. This paper investigates Object Referring with Spoken
Language (ORSpoken) by presenting two datasets and one novel approach. Objects
are annotated with their locations in images, text descriptions and speech
descriptions. This makes the datasets ideal for multi-modality learning. The
approach is developed by carefully taking down ORSpoken problem into three
sub-problems and introducing task-specific vision-language interactions at the
corresponding levels. Experiments show that our method outperforms competing
methods consistently and significantly. The approach is also evaluated in the
presence of audio noise, showing the efficacy of the proposed vision-language
interaction methods in counteracting background noise.Comment: 10 pages, Submitted to WACV 201
Encoding of phonology in a recurrent neural model of grounded speech
We study the representation and encoding of phonemes in a recurrent neural
network model of grounded speech. We use a model which processes images and
their spoken descriptions, and projects the visual and auditory representations
into the same semantic space. We perform a number of analyses on how
information about individual phonemes is encoded in the MFCC features extracted
from the speech signal, and the activations of the layers of the model. Via
experiments with phoneme decoding and phoneme discrimination we show that
phoneme representations are most salient in the lower layers of the model,
where low-level signals are processed at a fine-grained level, although a large
amount of phonological information is retain at the top recurrent layer. We
further find out that the attention mechanism following the top recurrent layer
significantly attenuates encoding of phonology and makes the utterance
embeddings much more invariant to synonymy. Moreover, a hierarchical clustering
of phoneme representations learned by the network shows an organizational
structure of phonemes similar to those proposed in linguistics.Comment: Accepted at CoNLL 201
Multimedia search without visual analysis: the value of linguistic and contextual information
This paper addresses the focus of this special issue by analyzing the potential contribution of linguistic content and other non-image aspects to the processing of audiovisual data. It summarizes the various ways in which linguistic content analysis contributes to enhancing the semantic annotation of multimedia content, and, as a consequence, to improving the effectiveness of conceptual media access tools. A number of techniques are presented, including the time-alignment of textual resources, audio and speech processing, content reduction and reasoning tools, and the exploitation of surface features
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