Identification of related languages from spoken data: Moving from off-line to on-line scenario

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F21%3A00009299" target="_blank" >RIV/46747885:24220/21:00009299 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0885230820301133" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0885230820301133</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.csl.2020.101180" target="_blank" >10.1016/j.csl.2020.101180</a>

Result language

angličtina

Original language name

Identification of related languages from spoken data: Moving from off-line to on-line scenario

Original language description

The accelerating flow of information we encounter around the world today makes many companies deploy speech recognition systems that, to an ever-growing extent, process data on-line rather than off-line. These systems, e.g., for real-time 24/7 broadcast transcription, often work with input-stream data containing utterances in more than one language. This multilingual data can correctly be transcribed in real-time only if the language used is identified with just a small latency for each input frame. For this purpose, a novel approach to on-line spoken language identification is proposed in this work. Its development is documented within a series of consecutive experiments starting in the off-line mode for 11 Slavic languages, going through artificially prepared multilingual data for the on-line scenario, and ending with real bilingual TV programs containing utterances in mutually similar Czech and Slovak. The resulting scheme that we propose operates frame-by-frame; it takes in a multilingual stream of speech frames and outputs a stream of the corresponding language labels. It utilizes a weighted finite-state transducer as a decoder, which smooths the output from a language classifier fed by multilingual and augmented bottleneck features. An essential factor from the accuracy point of view is that these features, as well as the classifier itself, are based on deep neural network architectures that allow the modeling of long-term time dependencies. The obtained results show that our scheme allows us to determine the language spoken in real-world bilingual TV shows with an average latency of around 2.5 seconds and with an increase in word error rate by a mere 2.9% over the reference 18.1% value yielded by using manually prepared language labels.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Computer Speech and Language

ISSN

0885-2308

e-ISSN

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Volume of the periodical

68

Issue of the periodical within the volume

JUL

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

19

Pages from-to

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UT code for WoS article

000629287300001

EID of the result in the Scopus database

2-s2.0-85098984452