Acoustically discriminative training for language models

This paper introduces a discriminative training for language models (LMs) by leveraging phoneme similarities estimated from an acoustic model. To train an LM discriminatively, we needed the correct word sequences and the recognized results that automatic speech recognition (ASR) produced by processing the utterances of those correct word sequences. But, sufficient utterances are not always available. We propose to generate the probable N-best lists, which the ASR may produce, directly from the correct word sequences by leveraging the phoneme similarities. We call this process the ldquoPseudo-ASRrdquo. We train the LM discriminatively by comparing the correct word sequences and the corresponding N-best lists from the Pseudo-ASR. Experiments with real-life data from a Japanese call center showed that the LM trained with the proposed method improved the accuracy of the ASR.