Spatiotemporal Encoding of Vowels in Noise Studied with the Responses of Individual Auditory-Nerve Fibers

The neural basis for robust speech perception exhibited by human listeners (e.g., across sound levels or background noises) remains unknown. The encoding of spectral shape based on auditory-nerve (AN) discharge rate degrades significantly at high sound levels, particularly in high spontaneousrate (SR) fibers (Sachs and Young 1979). However, continued support for rate coding has come from the observations that robust spectral coding occurs in some low-SR fibers for vowels in quiet and that rate-difference profiles provide enough information to account for behavioral discrimination of vowels (Conley and Keilson 1995; May, Huang, Le Prell, and Hienz 1996). Despite this support, it is clear that temporal codes are more robust than rate (Young and Sachs 1979), especially in noise (Delgutte and Kiang 1984; Sachs, Voigt, and Young 1983). Sachs et al. (1983) showed that rate coding in low-SR fibers was significantly degraded at a moderate signal-to-noise ratio for which human perception is robust. In contrast, temporal coding based on the average-localized-synchronized-rate (ALSR) remained robust. Although temporal coding based on ALSR is often shown to be robust, evidence for neural mechanisms to decode these cues is limited. Spatiotemporal mechanisms have been proposed for decoding these types of cues (e.g., Carney, Heinz, Evilsizer, Gilkey, and Colburn 2002; Deng and Geisler 1987; Shamma 1985). However, the detailed evaluation of spatiotemporal mechanisms has been limited primarily to modeling studies due to difficulties associated with the large population responses that are required to study spatiotemporal coding (e.g., see Palmer 1990). For example, Deng and Geisler (1987) used a transmission-line based AN model to suggest that spectral coding based on the peak cross-correlation between adjacent best-frequency (BF) channels was robust in the presence of background noise. In the present study, spectral coding of vowels in noise based on rate, ALSR, and a simple cross-BF coincidence detection scheme is evaluated from the responses of single AN fibers. By using data from a single AN fiber, many of the difficulties associated with large-population studies are eliminated.