Faculty of Information Technology, BUT

Publication Details

Semi-Supervised Training of Language Model on Spanish Conversational Telephone Speech Data

EGOROVA Ekaterina and SERRANO Jordi Lugue. Semi-Supervised Training of Language Model on Spanish Conversational Telephone Speech Data. In: Procedia Computer Science. Yogyakarta: Elsevier Science, 2016, pp. 114-120. ISSN 1877-0509. Available from: http://www.sciencedirect.com/science/article/pii/S1877050916300527
Czech title
Částečně kontrolované trénování jazykového modelu pro španělská konverzační telefonní řečová data
Type
conference paper
Language
english
Authors
Egorova Ekaterina, Ing. (DCGM FIT BUT)
Serrano Jordi Lugue (Telefónica)
URL
Keywords
Speech recognition, language modeling, semi-supervised learning
Abstract
This article is about semi-supervised training of language model on Spanish conversational telephone speech data.
Annotation
This work addresses one of the common issues arising when building a speech recognition system within a low-resourced scenario - adapting the language model on unlabeled audio data. The proposed methodology makes use of such data by means of semisupervised learning. Whilst it has been proven that adding system-generated labeled data for acoustic modeling yields good results, the benefits of adding system-generated sentence hypotheses to the language model are vaguer in the literature. This investigation focuses on the latter by exploring different criteria for picking valuable, well-transcribed sentences. These criteria range from confidence measures at word and sentence level to sentence duration metrics and grammatical structure frequencies. The processing pipeline starts with training a seed speech recognizer using only twenty hours of Fisher Spanish phone call conversations corpus. The proposed procedure attempts to augment this initial system by supplementing it with transcriptions generated automatically from unlabeled data with the use of the seed system. After generating these transcriptions, it is estimated how likely they are, and only the ones with high scores are added to the training data. Experimental results show improvements gained by the use of an augmented language model. Although these improvements are still lesser than those obtained from a system with only acoustic model augmentation, we consider the proposed system (with its low cost in terms of computational resources and the ability for task adaptation) an attractive technique worthy of further exploration.
Published
2016
Pages
114-120
Journal
Procedia Computer Science, vol. 2016, no. 81, ISSN 1877-0509
Proceedings
Procedia Computer Science
Conference
The 5th International Workshop on Spoken Language Technologies for Under-resourced Languages (SLTU'16), Yogyakarta, ID
Publisher
Elsevier Science
Place
Yogyakarta, ID
DOI
BibTeX
@INPROCEEDINGS{FITPUB11223,
   author = "Ekaterina Egorova and Lugue Jordi Serrano",
   title = "Semi-Supervised Training of Language Model on Spanish Conversational Telephone Speech Data",
   pages = "114--120",
   booktitle = "Procedia Computer Science",
   journal = "Procedia Computer Science",
   volume = 2016,
   number = 81,
   year = 2016,
   location = "Yogyakarta, ID",
   publisher = "Elsevier Science",
   ISSN = "1877-0509",
   doi = "10.1016/j.procs.2016.04.038",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/11223"
}
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