EARS: Electromyographical Automatic Recognition of Speech

In this paper, we present our research on automatic speech recognition of surface electromyographic signals that are generated by the human articulatory muscles. With parallel recorded audible speech and electromyographic signals, experiments are conducted to show the anticipatory behavior of electromyographic signals with respect to speech signals. Additionally, we demonstrate how to develop phone-based speech recognizers with carefully designed electromyographic feature extraction methods. We show that articulatory feature (AF) classifiers can also benefit from the novel feature, which improve the F-score of the AF classifiers from 0.467 to 0.686. With a stream architecture, the AF classifiers are then integrated into the decoding framework. Overall, the word error rate improves from 86.8 % to 29.9 % on a 100 word vocabulary recognition task.

WHISPERING SPEAKER IDENTIFICATION

This paper describes a study of automatically identifying whispering speakers. People usually whisper in order to avoid being identified or overheard by lowering their voices. The study compares performances between normal and whispered speech mode in clean and noisy environment under matched and mismatched training conditions, and describes the impact of feature warping and throat microphone on noise reduction. Score combination strategies are used when only little whisper data is available to improve performance. In sum, we achieved 8 % to 33 % relative improvements in identification accuracy with only 5 to 10 seconds noisy whispered speech data per speaker. 1