Proper name retrieval from diachronic documents for automatic speech transcription using lexical and temporal context

Proper names are usually key to understanding the information contained in a document. Our work focuses on increasing the vocabulary coverage of a speech transcription system by automatically retrieving new proper names from contemporary diachronic text documents. The idea is to use in-vocabulary proper names as an anchor to collect new linked proper names from the diachronic corpus. Our assumption is that time is an important feature for capturing name-to-context dependencies, that was confirmed by temporal mismatch experiments. We studied a method based on Mutual Information and proposed a new method based on cosine-similarity measure that dynamically augment the automatic speech recognition system vocabulary. Recognition results show a significant reduction of the word error rate using augmented vocabulary for broadcast news transcription.

[1]  Irina Illina,et al.  The automatic news transcription system: ANTS, some real time experiments , 2004, INTERSPEECH.

[2]  Marcello Federico,et al.  Broadcast news LM adaptation using contemporary texts , 2001, INTERSPEECH.

[3]  Andreas Stolcke,et al.  SRILM - an extensible language modeling toolkit , 2002, INTERSPEECH.

[4]  Kazuo Onoe,et al.  Time dependent language model for broadcast news transcription and its post-correction , 1998, ICSLP.

[5]  Georges Linarès,et al.  Person name recognition in ASR outputs using continuous context models , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[6]  Georges Linarès,et al.  Exploring temporal context in diachronic text documents for automatic OOV proper name retrieval , 2013, LTC 2013.

[7]  Georges Linarès,et al.  Local Methods for On-Demand Out-of-Vocabulary Word Retrieval , 2008, LREC.

[8]  Helmut Schmidt,et al.  Probabilistic part-of-speech tagging using decision trees , 1994 .

[9]  Denis Jouvet,et al.  Grapheme-to-Phoneme Conversion Using Conditional Random Fields , 2011, INTERSPEECH.

[10]  Alexandre Allauzen,et al.  Diachronic vocabulary adaptation for broadcast news transcription , 2005, INTERSPEECH.

[11]  Guillaume Gravier,et al.  The ESTER phase II evaluation campaign for the rich transcription of French broadcast news , 2005, INTERSPEECH.

[12]  Denis Maurel,et al.  Textual Similarity based on Proper Names , 2002 .

[13]  Marcello Federico,et al.  Lexicon adaptation for broadcast news transcription , 2001 .

[14]  Tatsuya Kawahara,et al.  Recent Development of Open-Source Speech Recognition Engine Julius , 2009 .

[15]  Mark Dredze,et al.  Contextual Information Improves OOV Detection in Speech , 2010, NAACL.

[16]  François Yvon,et al.  Les noms propres en traitement automatique de la parole , 2000 .