About vocabulary adaptation for automatic speech recognition of video data

Denis Jouvet (1) , David Langlois (2) , Mohamed Amine Menacer (2) , Dominique Fohr (1) , Odile Mella (1) , Kamel Smaïli (2)

1 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication
2 SMarT - Statistical Machine Translation and Speech Modelization and Text

This paper discusses the adaptation of vocabularies for automatic speech recognition. The context is the transcriptions of videos in French, English and Arabic. Baseline automatic speech recognition systems have been developed using available data. However, the available text data, including the GigaWord corpora from LDC, are getting quite old with respect to recent videos that are to be transcribed. The paper presents the collection of recent textual data from internet for updating the speech recognition vocabularies and training the language models, as well as the elaboration of development data sets necessary for the vocabulary selection process. The paper also compares the coverage of the training data collected from internet, and of the GigaWord data, with finite size vocabularies made of the most frequent words. Finally, the paper presents and discusses the amount of out-of-vocabulary word occurrences, before and after update of the vocabularies, for the three languages.

Mots clés: Speech recognition; vocabulary; vocabulary adaptation; vocabulary selection

Domaines: Traitement du signal et de l'image [eess.SP]

Denis Jouvet, David Langlois, Mohamed Amine Menacer, Dominique Fohr, Odile Mella, et al.. About vocabulary adaptation for automatic speech recognition of video data. ICNLSSP'2017 - International Conference on Natural Language, Signal and Speech Processing, Dec 2017, Casablanca, Morocco. pp.1-5. ⟨hal-01649057⟩