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Speech translation,
AT&T Research is developing a real-time speech-to-speech translation technology that begins translating as soon as it detects speech.
SPECTRA: A SPEECH-TO-SPEECH TRANSLATION SYSTEM IN THE CLOUD
Vivek Rangarajan sridhar, Srinivas Bangalore, Aura Jimenez, Laden Golipour, Prakash Kolan
IEEE International Conference on Emerging Signal Processing Applications,
2012.
[PDF]
[BIB]
IEEE Copyright
This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in IEEE International Conference on Emerging Signal Processing Applications. , 2012-01-12
{In this demonstration, we will present Spectra, a speech-to-speech (S2S) translation system in the cloud. Spectra comprises of an HMM-based large vocabulary continuous speech recognition (AT&T Watson Speech Recognizer), a phrase-based translation
system, and, a unit selection text-to-speech synthesis system (AT&T Natural Voices TTS). Spectra currently runs on any iOS device and can be downloaded as an application from the Apple application store (http://itunes.apple.com/us/app/spectra/id432494549?mt=8). Spectra is endowed with automatic language identification capabilities and can currently translate from/to English and six other languages (Chinese, French, German, Italian, Japanese and Spanish).}
Real-time Incremental Speech-to-Speech Translation of Dialogs
Vivek Rangarajan sridhar, Srinivas Bangalore, Prakash Kolan, Ladan Golipour, Aura Jimenez
Proceedings of NAACL-HLT,
NAACL-HLT 2012,
2012.
[PDF]
[BIB]
Enriching text-to-speech synthesis using automatic dialog act tags
Vivek Kumar, Ann Syrdal, Alistair Conkie, Srinivas Bangalore
Interspeech,
2011.
[BIB]
{We present an approach for enriching dialog based text-to-speech (TTS) synthesis systems by explicitly controlling the expressiveness through the use of dialog act tags. The dialog act tags in our framework are automatically obtained by training a maximum entropy classifier on the Switchboard-DAMSL data set, unrelated to the TTS database. We compare the voice quality produced by exploiting automatic dialog act tags with that using human annotations of dialog acts, and with two forms of reference databases. Even though the inventory of tags is different for the automatic tagger and human annotation, exploiting either form of dialog markup generates better voice quality in comparison with the reference voices in subjective evaluation. }
Crawling Back and Forth: Using Back and Out Links to Locate Bilingual Sites
Luciano Barbosa, Srinivas Bangalore, Vivek Kumar
IJCNLP,
2011.
[PDF]
[BIB]
AFNLP Copyright
The definitive version was published in IJCNLP. , 2011-11-15
The definitive version was published in Very Large Databases, 2011. , 2011-11-15
{Recently, there has been an increase interested for Web parallel
text for tasks such as machine translation and cross-language information
retrieval. Although previous
works have addressed many aspects of it, including
document pair selection, and sentence and word alignment, the
problem of discovering bilingual data sources in a large
scale has been overlooked to a great extent.
In this paper, we propose a novel crawling strategy to locate
bilingual sites which aims to achieve a balance between the
two conflicting requirements of this problem: the need to perform
a broad search while at the same time avoiding the need to crawl
unproductive Web regions. Our solution does so by focusing on
the graph neighborhood of bilingual sites and exploring
the patterns of the links in this region to guide its visitation policy.
To detect such sites, we introduce a two-step strategy that, first, relies on common patterns
found in the internal links of these sites to compose a classifier
that identifies candidate pages as entry points to parallel data in these sites,
and then, verifies whether these pages are in fact in the languages
of interest. Our experimental evaluation show that our crawler outperforms previous
crawling approaches for this task and produces a
high-quality collection of bilingual sites.
}

A Scalable Approach to Building a Parallel Corpus from the Web
Vivek Kumar, Luciano Barbosa, Srinivas Bangalore
INTERSPEECH,
2011.
[PDF]
[BIB]
ACL Copyright
The definitive version was published in EMNLP. , 2011-08-27
{Parallel text acquisition from the Web is an attractive way for
augmenting statistical models (e.g., machine translation, cross-
lingual document retrieval) with domain representative data.
The basis for obtaining such data is a collection of pairs of bilin-
gual Web sites or pages. In this work, we propose a crawling
strategy that locates bilingualWeb sites by constraining the vis-
itation policy of the crawler to the graph neighborhood of bilin-
gual sites on the Web. Subsequently, we use a novel recursive
mining technique that recursively extracts text and links from
the collection of bilingual Web sites obtained from the crawl-
ing. Our method does not suffer from the computationally pro-
hibitive combinatorial matching typically used in previous work
that uses document retrieval techniques to match a collection of
bilingual webpages. We demonstrate the efficacy of our ap-
proach in the context of machine translation in the tourism and
hospitality domain. The parallel text obtained using our novel
crawling strategy results in a relative improvement of 21% in
BLEU score (English-to-Spanish) over an out-of-domain seed
translation model trained on the European parliamentary pro-
ceedings.}