Speaker-Independent Name Recognition Using Improved Compensation and Acoustic Modeling Methods for Mobile Applications

Name recognition is an important application of automatic speech recognition in embedded devices. Since embedded devices are used in diverse environments, noise robustness is very important. Moreover, unlike normal computer-based speech recognition applications, embedded speech recognition must deal with problems arising from limited resources. Facing these challenges, we have developed environment compensation and acoustic modeling techniques that improve robustness and accuracy of a speaker-independent name recognition system in hands-free conditions. These techniques are efficient to implement and are effective for performance improvement. On a name recognition task, we observed more than 53% word error rate reduction, compared to a baseline system. These improvements were obtained with minimal increase of resources