A connectionist model for consonant-vowel syllable recognition

The authors describe preliminary CV (consonant-vowel syllable) recognition experiments using neural network learning and retrieval paradigms. They have trained both one and two speaker systems and report on the results of both speaker dependent and speaker independent testing. The one-speaker systems performed at 94 percent correct classifying the three voiced stop consonants learned in 3 different vowel contexts using 40ms of data from burst onset per CV token (all results involve tokens not used in training). Vowel performance was also good when at least 55 ms of data from each CV were used. The system receives no segmentation information on consonant-vowel boundary. When both voiced and voiceless CV types were learned together by a one-speaker system, consonant performance dropped to just over 80 percent. This fall-off was mostly due to weak performance on velar stops. Two-speaker systems trained on the voiced CV tokens in three vowel contexts also performed at or above 90 percent. The two-speaker system also demonstrated speaker independent ability with over 80 percent correct consonant classification with voiced CV tokens from a third speaker