Memorias de investigación
Research Publications in journals:
Topic identification techniques applied to dynamic language model adaptation for automatic speech recognition
Year:2015

Research Areas
  • Electronic technology and of the communications,
  • Information technology and adata processing

Information
Abstract
In this paper we present an efficient speech recognition approach for multitopic speech by combining information retrieval techniques and topic-based language modeling. Information retrieval based techniques, such as topic identification by means of Latent Semantic Analysis, are used to identify the topic in a recognized transcription of an audio segment. According to the confidence on the topics that have been identified, we propose a dynamic language model adaptation in order to improve the recognition performance in ?a two stages? automatic speech recognition system. The scheme used for the adaptation of the language model is a linear interpolation between a background general LM and a topic dependent LM. We have studied different approaches to generate the topic dependent LM and also for determining the interpolation weight of this model with the background model. In one of these approaches we use the given topic labels in the training dataset to obtain the topic models. In the other approach we separate the documents in the training dataset into topic clusters by using the k-means algorithm. For strengthening the adaptation models we also use topic identification techniques to group non topic-labeled documents from the EUROPARL text database in order to increase the amount of data for training specific topic based language models. For the evaluation of the proposed system we are using the Spanish partition of the European Parliament Plenary Sessions (EPPS) Database; we selected a subset of the database with 67 labeled topics for the evaluation. For the task of topic identification our experiments show a relative reduction in topic identification error of 44.94% when compared to the baseline method, the Generalized Vector Model with a classic TF?IDF weighting scheme. For the task of dynamic adaptation of LMs applied to ASR we have achieved a relative reduction in WER of 13.52% over a single background language model.
International
Si
JCR
Si
Title
Expert Systems With Applications
ISBN
0957-4174
Impact factor JCR
2,24
Impact info
Datos JCR del año 2013
Volume
42
10.1016/j.eswa.2014.07.035
Journal number
From page
101
To page
112
Month
ENERO
Ranking
Journal Rank in Category 12/81
Participants

Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Tecnología del Habla
  • Departamento: Ingeniería Electrónica