Effects of word frequency and phonological neighborhood density on speech recognition in background noise and competing speech

Abstract

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.]