Incorporating Named Entity Recognition into the Speech Transcription Process

Mohamed Hatmi (1, 2) , Christine Jacquin (1) , Emmanuel Morin (1) , Sylvain Meignier (2)

1 LINA - Laboratoire d'Informatique de Nantes Atlantique
2 LIUM - Laboratoire d'Informatique de l'Université du Mans

Named Entity Recognition (NER) from speech usually involves two sequential steps: transcribing the speech using Automatic Speech Recognition (ASR) and annotating the outputs of the ASR process using NER techniques. Recognizing named entities in automatic transcripts is difficult due to the presence of transcription errors and the absence of some important NER clues, such as capitalization and punctuation. In this paper, we describe a methodology for speech NER which consists of incorporating NER into the ASR process so that the ASR system generates transcripts annotated with named entities. The combination is achieved by adapting ASR language models and pre-annotating the pronunciation dictionary. We evaluate this method on ESTER 2 corpus, and show significant improvements over traditional approaches.

Mots clés: Named Entity Recognition; Automatic Speech Recognition; language modeling; ASR vocabulary

Domaines: Informatique et langage [cs.CL]

Mohamed Hatmi, Christine Jacquin, Emmanuel Morin, Sylvain Meignier. Incorporating Named Entity Recognition into the Speech Transcription Process. Interspeech, 2013, Lyon, France. ⟨hal-01433438⟩