New uses for the N-Best sentence hypotheses within the BYBLOS speech recognition system
- 1 January 1992
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 1 (15206149) , 1-4 vol.1
- https://doi.org/10.1109/icassp.1992.225987
Abstract
The authors describe four different ways in which they used the N-Best paradigm within the BYBLOS system. The most obvious use is for the efficient integration of speech recognition with a linguistic natural language understanding module. However, the authors have extended this principle to several other acoustic knowledge sources. They also describe a simple and efficient means for investigating and incorporating arbitrary knowledge sources. The N-Best hypotheses are used to provide close alternatives for discriminative training. Finally, the authors have developed a simple technique that allows them to optimize several weights and parameters within a system in a way that directly minimizes word error rate. Examples of each of these uses within the BYBLOS system are described.<>Keywords
This publication has 9 references indexed in Scilit:
- Tied mixture continuous parameter models for large vocabulary isolated speech recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Maximum mutual information estimation of HMM parameters for continuous speech recognition using the N-best algorithmPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- The N-best algorithms: an efficient and exact procedure for finding the N most likely sentence hypothesesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Speech recognition using segmental neural netsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1992
- Integration of diverse recognition methodologies through reevaluation of N-best sentence hypothesesPublished by Association for Computational Linguistics (ACL) ,1991
- A comparison of several approximate algorithms for finding multiple (N-best) sentence hypothesesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1991
- The forward-backward search algorithmPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1991
- A tree-trellis based fast search for finding the N-best sentence hypotheses in continuous speech recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1991
- Semi-continuous hidden Markov models for speech signalsComputer Speech & Language, 1989