The Beauty of Errors: Patterns of Error Correction in Desktop Speech Systems

Automatic Speech Recognition (ASR) systems have improved greatly over the last three decades. However, even with 98% reported accuracy, error correction still consumes a significant portion of user effort in text creation tasks. We report on data collected during a study of three commercially available ASR systems that show how initial users of speech systems tend to fixate on a single strategy for error correction. This tendency coupled with application assumptions about how error correction features will be used, combine to make a very frustrating, and unsatisfying user experience. We observe two distinct error correction patterns: spiral depth (Oviatt & VanGent, 1996) and cascades. In contrast, users with more extensive experience learn to switch correction strategies more quickly.