Abstract
This paper explores a better way to learn word vector representations for language identification (LID). We have focused on a phonotactic approach using phoneme sequences in order to make phonotactic units (phone-grams) to incorporate context information. In order to take into consideration the morphology of phone-grams, we have considered the use of sub-word information (lower-order n-grams) to learn phone-grams embeddings using FastText. These embeddings are used as input to an i-Vector framework to train a multiclass logistic classifier. Our approach has been compared with a LID system that uses phone-gram embeddings learned through Skipgram that do not implement sub-word information, using Cavg as a metric for our experiments. Our approach to LID to incorporate sub-word information in phone-grams embeddings significantly improves the results obtained by using embeddings that are learned ignoring the structure of phone-grams. Furthermore, we have shown that our system provides complementary information to an acoustic system, improving it through the fusion of both systems.
Original language | English |
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Title of host publication | Conversational Dialogue Systems for the Next Decade, IWSDS 2020 |
Editors | Luis Fernando D’Haro, Zoraida Callejas, Satoshi Nakamura |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 339-348 |
Number of pages | 10 |
ISBN (Print) | 9789811583940 |
DOIs | |
State | Published - 2021 |
Event | 11th International Workshop on Spoken Dialogue Systems, IWSDS 2020 - Madrid, Spain Duration: 21 Sep 2020 → 23 Sep 2020 |
Publication series
Name | Lecture Notes in Electrical Engineering |
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Volume | 704 |
ISSN (Print) | 1876-1100 |
ISSN (Electronic) | 1876-1119 |
Conference
Conference | 11th International Workshop on Spoken Dialogue Systems, IWSDS 2020 |
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Country/Territory | Spain |
City | Madrid |
Period | 21/09/20 → 23/09/20 |
Bibliographical note
Publisher Copyright:© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.