Detail publikace

Analysis of DNN-based Embeddings for Language Recognition on the NIST LRE 2017

LOZANO DÍEZ, A. PLCHOT, O. MATĚJKA, P. NOVOTNÝ, O. GONZALEZ-RODRIGUEZ, J.

Originální název

Analysis of DNN-based Embeddings for Language Recognition on the NIST LRE 2017

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

In this work, we analyze different designs of a language identification (LID) system based on embeddings. In our case, an embedding represents a whole utterance (or a speech segment of variable duration) as a fixed-length vector (similar to the ivector). Moreover, this embedding aims to capture information relevant to the target task (LID), and it is obtained by training a deep neural network (DNN) to classify languages. In particular, we trained a DNN based on bidirectional long short-term memory (BLSTM) recurrent neural network (RNN) layers, whose frame-by-frame outputs are summarized into mean and standard deviation statistics for each utterance. After this pooling layer, we add two fully connected layers whose outputs are used as embeddings, which are afterwards modeled by a Gaussian linear classifier (GLC). For training, we add a softmax output layer and train the whole network with multi-class cross-entropy objective to discriminate between languages. We analyze the effect of using data augmentation in the DNN training, as well as different input features and architecture hyper-parameters, obtaining configurations that gradually improved the performance of the embedding system. We report our results on the NIST LRE 2017 evaluation dataset and compare the performance of embeddings with a reference i-vector system. We show that the best configuration of our embedding system outperforms the strong reference i-vector system by 3% relative, and this is further pushed up to 10% relative improvement via a simple score level fusion.

Klíčová slova

language recognition

Autoři

LOZANO DÍEZ, A.; PLCHOT, O.; MATĚJKA, P.; NOVOTNÝ, O.; GONZALEZ-RODRIGUEZ, J.

Vydáno

26. 6. 2018

Nakladatel

International Speech Communication Association

Místo

Les Sables d'Olonne

ISSN

2312-2846

Periodikum

Proceedings of Odyssey: The Speaker and Language Recognition Workshop Odyssey 2014, Joensuu, Finland

Ročník

2018

Číslo

6

Stát

Finská republika

Strany od

39

Strany do

46

Strany počet

8

URL

https://www.isca-speech.org/archive/Odyssey_2018/pdfs/42.pdf

BibTex

@inproceedings{BUT155066,
  author="Alicia {Lozano Díez} and Oldřich {Plchot} and Pavel {Matějka} and Ondřej {Novotný} and Joaquin {Gonzalez-Rodriguez}",
  title="Analysis of DNN-based Embeddings for Language Recognition on the NIST LRE 2017",
  booktitle="Proceedings of Odyssey 2018 The Speaker and Language Recognition Workshop",
  year="2018",
  journal="Proceedings of Odyssey: The Speaker and Language Recognition Workshop Odyssey 2014, Joensuu, Finland",
  volume="2018",
  number="6",
  pages="39--46",
  publisher="International Speech Communication Association",
  address="Les Sables d'Olonne",
  doi="10.21437/Odyssey.2018-6",
  issn="2312-2846",
  url="https://www.isca-speech.org/archive/Odyssey_2018/pdfs/42.pdf"
}

Dokumenty

lozano_odyssey2018_42.pdf