In this paper we present an approach to recognition confidence scoring and a method for integrating confidence scores into the understanding and dialogue components of a speech understanding system. The system uses a multi-tiered approach where confidence scores are computed at the phonetic, word, and utterance levels. The scores are produced by extracting confidence features from the computation of the recognition hypotheses and processing these features using an accept/reject classifier for word and utterance hypotheses. The output of the confidence classifiers can then be incorporated into the parsing mechanism of the language understanding component. To evaluate the system, experiments were conducted using the JUPITER weather information system. Evaluation was performed at the understanding level using key-value pair concept error rate as the evaluation metric. When confidence scores were integrated into the understanding component of the system, the concept error rate was reduced by over 35%.

Confidence measures are computed to estimate the certainty that target acoustic units are spoken in specific speech segments. They are applied in tasks such as keyword verification or utterance verification. Because many of the confidence measures use the same set of models and features as in recognition, the resulting scores may not provide an independent measure of reliability. In this paper, we propose two articulatory feature (AF) based phoneme confidence measures that estimate the acoustic reliability based on the match in AF properties. While acoustic-based features, such as Mel-frequency cepstral coefficients (MFCC), are widely used in speech processing, some recent works have focus on linguistically based features, such as the articulatory features that relate directly to the human articulatory process which may better capture speech characteristics. The articulatory features can either replace or complement the acoustic-based features in speech processing. The proposed AF-based measures in this paper were evaluated, in comparison and in combination, with the HMM-based scores on phoneme and keyword verification tasks using childrenÕs speech collected for a computer-based English pronunciation learning project. To fully evaluate their usefulness, the proposed measures and combinations were evaluated on both native and non-native data; and under field test conditions that mis-matches with the training condition. The experimental results show that under the different environments, combinations of the AF scores with the HMM-based

In previous work, we experimented with different techniques of vector-based call routing, using the transcriptions of the queries to compare algorithms. In this paper, we base the routing decisions on the recogniser output rather than transcriptions and examine the use of confidence measures (CMs) to combat the problems caused by the "noise" in the recogniser output. CMs are derived for both the words output from the recogniser and for the routings themselves and are used to investigate improving both routing accuracy and routing confidence. Results are given for a 35 route retail store enquiry-point task. They suggest that although routing error is controlled by the recogniser errorrate, confidence in routing decisions can be improved using these techniques.

In this paper we present an approach to recognition confidence scoring and a method for integrating confidence scores into the understanding and dialogue components of a speech understanding system. The system uses a multi-tiered approach where confidence scores are computed at the phonetic, word, and utterance levels. The scores are produced by extracting confidence features from the computation of the recognition hypotheses and processing these features using an accept/reject classifier for word and utterance hypotheses. The output of the confidence classifiers can then be incorporated into the parsing mechanism of the language understanding component. To evaluate the system, experiments were conducted using the JUPITER weather information system. Evaluation was performed at the understanding level using key-value pair concept error rate as the evaluation metric. When confidence scores were integrated into the understanding component of the system, the concept error rate was reduced by over 35%.