Title :
Unsupervised language model adaptation for broadcast news
Author :
Chen, Lungzhou ; Gauvain, Jean-Luc ; Lamel, Lori ; Adda, Gilles
Author_Institution :
Spoken Language Process. Group, LIMSI-CNRS, Orsay, France
Abstract :
Unsupervised language model adaptation for speech recognition is challenging, particularly for complicated tasks such the transcription of broadcast news (BN) data. This paper presents an unsupervised adaptation method for language modeling based on information retrieval techniques. The method is designed for the broadcast news transcription task where the topics of the audio data cannot be predicted in advance. Experiments are carried out using the LIMSI American English BN transcription system and the NIST 1999 BN evaluation sets. The unsupervised adaptation method reduces the perplexity by 7% relative to the baseline LM and yields a 2% relative improvement for a 10xRT system.
Keywords :
information retrieval; natural languages; speech recognition; LIMSI American English BN transcription system; broadcast news; information retrieval techniques; language modeling; perplexity; speech recognition; unsupervised language model adaptation; Adaptation model; Broadcasting; Data mining; Decoding; Design methodology; Natural languages; Speech recognition; TV; Text recognition; Training data;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on
Print_ISBN :
0-7803-7663-3
DOI :
10.1109/ICASSP.2003.1198757
