New uses for the N-Best sentence hypotheses within the BYBLOS speech recognition system

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.< >