Different word representations and their combination for proper name retrieval from diachronic documents

Irina Illina (1) , Dominique Fohr (1)

1 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication

This paper deals with the problem of high-quality transcription systems for very large vocabulary automatic speech recognition (ASR). We investigate the problem of automatic retrieval of out-of-vocabulary (OOV) proper names (PNs). We want to take into account the temporal, syntactic and semantic context of words. Nowadays, Artificial Neural Networks (NN) are widely used in natural language processing: continuous space representations of words is learned automatically from unstructured text data. To model the latent topics at document level, Latent Dirichlet Allocation (LDA) has been successful. In this paper, we propose OOV PN retrieval using (1) temporal versus topic context modeling; (2) different word representation spaces for word-level and document-level context modeling; (3) combinations of retrieval results. Experimental evaluation on broadcast news data shows that the proposed method combinations lead to better results. This confirms the complementarity of methods.

Mots clés: speech recognition; neural networks; LDA; vocabulary extension; out-of-vocabulary words; proper names

Domaines: Interface homme-machine [cs.HC]

Irina Illina, Dominique Fohr. Different word representations and their combination for proper name retrieval from diachronic documents. IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2015) , Dec 2015, Scottsdale, United States. ⟨hal-01201533⟩