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Out-of-vocabulary (OOV) words can pose a particular problem for automatic speech recognition (ASR) of broadcast news. The language models (LMs) of ASR systems are typically trained on static corpora, whereas new words (particularly new proper nouns) are continually introduced in the media. Additionally, such OOVs are often content-rich proper nouns that are vital to understanding the topic. In this...
We present an intelligent sample selection approach to language model adaptation for handwritten text recognition, which exploits a combination of in-domain and out-of-domain data for construction of language models. In comparison to approaches proposed in the literature, our approach is characterized by a careful consideration of the criteria used for ranking samples and an innovative approach to...
A method for semi-supervised language modeling, which was designed to improve the robustness of a language model (LM) obtained from manually transcribed (labeled) data, is proposed. The LM is implemented as a log-linear model, which employs a set of linguistic features derived from word or phoneme n-grams. The proposed method is formulated as a multi-objective optimization programming problem (MOP),...
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