Toward a detector-based universal phone recognizer

In recent research, we have proposed a high-accuracy bottom-up detection-based paradigm for continuous phone speech recognition. The key component of our system was a bank of articulatory detectors each of which computes a score describing an activation level of the specified speech phonetic features that the current frame exhibits. In this work, we present our first attempt at designing a universal phone recognizer using the detection-based approach. We show that our technique is intrinsically language independent since reliable articulatory detectors can be designed for diverse languages, and robust detection can be performed across languages. Moreover, a universal set of detectors is designed by sharing the training material available for several diverse languages. We further demonstrate that our approach makes it possible to decode new target languages by neither retraining nor applying acoustic adaptation techniques. We report phone recognition performance that compares favorably with the best results known by the authors on the OGI Multi-language Telephone Speech corpus.

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