Class-based n-gram models of natural language
Computational Linguistics
Statistical methods for speech recognition
Statistical methods for speech recognition
Dialogue management in the Mercury flight reservation system
ANLP/NAACL-ConvSyst '00 Proceedings of the 2000 ANLP/NAACL Workshop on Conversational systems - Volume 3
A variable-length category-based n-gram language model
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Tools and Technologies for Computer-Aided Speech and Language Therapy
Speech Communication
Analysis of acoustic features in speakers with cognitive disorders and speech impairments
EURASIP Journal on Advances in Signal Processing - Special issue on analysis and signal processing of oesophageal and pathological voices
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In this paper, a task of human-machine interaction based on speech is presented. The specific task consists on the use and control of a set of home appliances through a turn-based dialogue system. This work focuses on the first part of the dialogue system, the Automatic Speech Recognition (ASR) system. Two lines of work are taken into account to improve the performance of the ASR system. On one hand, the acoustic modeling required for the ASR is improved via Speaker Adaptation techniques. On the other hand, the Language Modeling in the system is improved by the use of class-based Language Models. The results show the good performance of both techniques to improve the ASR results, as the Word Error Rate (WER) drops from 5.81% using a close-talk microphone to a 0.99% and from 14.53% using a lapel microphone to a 1.52%. Also, an important reduction is achieved in terms of the Category Error Rate (CER), which measures the ability of the ASR system to extract the semantic information of the uttered sentence, dropping from 6.13% and 15.32% to 1.29% and 1.32% for the two microphones used in the experiments.