Recognition Performance of a Structured Language Model
<|reference_start|>Recognition Performance of a Structured Language Model: A new language model for speech recognition inspired by linguistic analysis is presented. The model develops hidden hierarchical structure incrementally and uses it to extract meaningful information from the word history - thus enabling the use of extended distance dependencies - in an attempt to complement the locality of currently used trigram models. The structured language model, its probabilistic parameterization and performance in a two-pass speech recognizer are presented. Experiments on the SWITCHBOARD corpus show an improvement in both perplexity and word error rate over conventional trigram models.<|reference_end|>
@article{chelba2000recognition,
    title={Recognition Performance of a Structured Language Model},
    author={Ciprian Chelba, Frederick Jelinek (CLSP The Johns Hopkins University)},
    journal={Proceedings of Eurospeech, 1999, pp. 1567-1570, Budapest, Hungary},
    year={2000},
    archivePrefix={arXiv},
    eprint={cs/0001022},
    primaryClass={cs.CL}
}