These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Auditory-visual speech recognition by hearing-impaired subjects: consonant recognition, sentence recognition, and auditory-visual integration.
    Author: Grant KW, Walden BE, Seitz PF.
    Journal: J Acoust Soc Am; 1998 May; 103(5 Pt 1):2677-90. PubMed ID: 9604361.
    Abstract:
    Factors leading to variability in auditory-visual (AV) speech recognition include the subject's ability to extract auditory (A) and visual (V) signal-related cues, the integration of A and V cues, and the use of phonological, syntactic, and semantic context. In this study, measures of A, V, and AV recognition of medial consonants in isolated nonsense syllables and of words in sentences were obtained in a group of 29 hearing-impaired subjects. The test materials were presented in a background of speech-shaped noise at 0-dB signal-to-noise ratio. Most subjects achieved substantial AV benefit for both sets of materials relative to A-alone recognition performance. However, there was considerable variability in AV speech recognition both in terms of the overall recognition score achieved and in the amount of audiovisual gain. To account for this variability, consonant confusions were analyzed in terms of phonetic features to determine the degree of redundancy between A and V sources of information. In addition, a measure of integration ability was derived for each subject using recently developed models of AV integration. The results indicated that (1) AV feature reception was determined primarily by visual place cues and auditory voicing + manner cues, (2) the ability to integrate A and V consonant cues varied significantly across subjects, with better integrators achieving more AV benefit, and (3) significant intra-modality correlations were found between consonant measures and sentence measures, with AV consonant scores accounting for approximately 54% of the variability observed for AV sentence recognition. Integration modeling results suggested that speechreading and AV integration training could be useful for some individuals, potentially providing as much as 26% improvement in AV consonant recognition.
    [Abstract] [Full Text] [Related] [New Search]