Liu, XA and Byrne, WJ and Gales, MJF and De Gispert, A and Tomalin, M and Woodland, PC and Yu, K (2007) Discriminative language model adaptation for Mandarin broadcast speech transcription and translation. In: UNSPECIFIED pp. 153-158..Full text not available from this repository.
This paper investigates unsupervised test-time adaptation of language models (LM) using discriminative methods for a Mandarin broadcast speech transcription and translation task. A standard approach to adapt interpolated language models to is to optimize the component weights by minimizing the perplexity on supervision data. This is a widely made approximation for language modeling in automatic speech recognition (ASR) systems. For speech translation tasks, it is unclear whether a strong correlation still exists between perplexity and various forms of error cost functions in recognition and translation stages. The proposed minimum Bayes risk (MBR) based approach provides a flexible framework for unsupervised LM adaptation. It generalizes to a variety of forms of recognition and translation error metrics. LM adaptation is performed at the audio document level using either the character error rate (CER), or translation edit rate (TER) as the cost function. An efficient parameter estimation scheme using the extended Baum-Welch (EBW) algorithm is proposed. Experimental results on a state-of-the-art speech recognition and translation system are presented. The MBR adapted language models gave the best recognition and translation performance and reduced the TER score by up to 0.54% absolute. © 2007 IEEE.
|Item Type:||Conference or Workshop Item (UNSPECIFIED)|
|Uncontrolled Keywords:||Discriminative training Language model adaptation Speech recognition and translation|
|Divisions:||Div F > Machine Intelligence|
|Depositing User:||Unnamed user with email firstname.lastname@example.org|
|Date Deposited:||16 Jul 2015 13:53|
|Last Modified:||04 Aug 2015 22:23|