Publication Details
PLLR Features in Language Recognition System for RATS
Diez Sánchez Mireia, M.Sc., Ph.D. (DCGM)
Soufifar Mehdi Mohammad, Ing.
Burget Lukáš, doc. Ing., Ph.D. (DCGM)
language recognition, PLLR, iVector, phone posterior, neural network, RATS
This article is about PLLR Features in Language Recognition System for RATS (Robust Automatic Transcription of Speech).
In this paper, we study the use of features based on frame-byframe phone posteriors (PLLRs) for language recognition. The results are reported on the datasets developed for the DARPA RATS (Robust Automatic Transcription of Speech) program, which seeks to advance state of the art detection capabilities on audio from highly degraded communication channels. We show that systems based on the PLLRs outperform the standard acoustic system based on PLP2 features. By experimenting with the system combinations, we also demonstrate that the PLLR-based systems contain complementary information with respect to the PLP2 system. Finally we make a comparison between the PLLR and phonotactic systems with the outcome favorable to the PLLR.
@inproceedings{BUT111669,
author="Oldřich {Plchot} and Mireia {Diez Sánchez} and Mehdi Mohammad {Soufifar} and Lukáš {Burget}",
title="PLLR Features in Language Recognition System for RATS",
booktitle="Proceedings of Interspeech 2014",
year="2014",
pages="3048--3051",
publisher="International Speech Communication Association",
address="Singapore",
isbn="978-1-63439-435-2",
url="http://www.isca-speech.org/archive/interspeech_2014/i14_3047.html"
}