Publication detail

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.

Original Title

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

Type

conference paper

Language

English

Original Abstract

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.

Keywords

language recognition

Authors

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

Released

26. 6. 2018

Publisher

International Speech Communication Association

Location

Les Sables d'Olonne

ISBN

2312-2846

Periodical

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

Year of study

2018

Number

6

State

Republic of Finland

Pages from

39

Pages to

46

Pages count

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"
}

Documents

lozano_odyssey2018_42.pdf