A framework of performance evaluation and error analysis methodology for speech understanding systems

With improved speech understanding technology, many successful working systems have been developed. However, the high degree of complexity and wide variety of design methodology make the performance evaluation and error analysis for such systems very difficult. The different metrics for individual modules such as the word accuracy, spotting rate, language model coverage and slot accuracy are very often helpful, but it is always difficult to select or tune each of the individual modules or determine which module contributed to how much percentage of understanding errors based on such metrics. In this paper, a new framework for performance evaluation and error analysis for speech understanding systems is proposed based on the comparison with the ‘best-matched’ references obtained from the word graphs with the target words and tags given. In this framework, all test utterances can be classified based on the error types, and various understanding metrics can be obtained accordingly. Error analysis approaches based on an error plane are then proposed, with which the sources for understanding errors (e.g. poor acoustic recognition, poor language model, search error, etc.) can be identified for each utterance. Such a framework will be very helpful for design and analysis of speech understanding systems.