Effects of word frequency and phonological neighborhood density on speech recognition in background noise and competing speech
Nicole K. Whittle, Christian Herrera Ortiz, Marjorie R. Leek, Jerome Heidrich, Mark Jenkins, Jonathan Henry Venezia · The Journal of the Acoustical Society of America · 2021
Clinical speech-in-noise tests typically use materials without contextual constraint or balanced for linguistic properties like word/phoneme frequency. However, real-world linguistic context effects can be substantial and vary by listener and scenario. Here, 38 participants completed the Theo-Victor-Michael (TVM) speech test in four types of background: speech shaped noise (SSN), speech-envelope modulated noise (envSSN), one competing talker (1T), and two competing talkers (2T) (Helfer and Freyman, 2009). The TVM is a matrix test using keywords from a corpus of one- and two- syllable nouns that vary considerably in word frequency (FREQ) and phonological neighborhood density (DENS). Bayesian logistic regression was used to estimate the effects of FREQ/DENS on TVM performance. A multinomial model was used for 1T/2T to assess reporting of target and distractor keywords. Overall, percent-correct recognition increased with increasing keyword FREQ and decreased with increasing keyword DENS. Effects were larger in SSN/envSSN than 1T/2T. Statistically significant but small effects of FREQ/DENS were observed on distractor responses in 1T/2T. Adjusting performance for FREQ/DENS substantially shifted the distribution of scores but only for SSN/envSSN. Performance in 1T/2T may be dominated by non-linguistic factors, and/or less sensitive to FREQ/DENS due to higher difficulty or linguistic competition from the background talkers. [Work supported by VA RR&D Service.]