Automatic speech recognition based on property detectors

Abstract

Speaker-independent, large-vocabulary, continuous speech recognition by a machine is a challenging problem for which over a decade of search has been made without significant progress. In the existing systems, the same acoustic feature vector (LPC, cepstrum, filter bank, etc.) is used for all speech sounds and they heavily depend on contextual information for their success. This paper presents some results based on a radically different approach called “property detectors.” The approach of property detectors is well known in visual perception where it has been demonstrated that specialized detectors exist on the retina that trigger only for vertical, horizontal, or inclined lines. It has only been speculated that such specialized detectors could exist for speech. Recently, acoustic properties have been discovered that uniquely characterize some phonemes like /a/, /i/, /u/, /e/, /o/, and /s/. A limited-vocabulary, speaker-independent airline schedule announcement system was developed. This system was tested in a noisy hall with a large number of speakers, including female speakers, with different linguistic backgrounds. The system, though is in its early stage, gave a performance of about 85% accuracy. The approach based on property detectors aappears promising