Why are listeners hindered by talker variability?

Abstract

Though listeners readily recognize speech from a variety of talkers, accommodating talker variability comes at a cost: Myriad studies have shown that listeners are slower to recognize a spoken word when there is talker variability compared with when talker is held constant. This review focuses on two possible theoretical mechanisms for the emergence of these processing penalties. One view is that multitalker processing costs arise through a resource-demanding talker accommodation process, wherein listeners compare sensory representations against hypothesized perceptual candidates and error signals are used to adjust the acoustic-to-phonetic mapping (an active control process known as contextual tuning). An alternative proposal is that these processing costs arise because talker changes involve salient stimulus-level discontinuities that disrupt auditory attention. Some recent data suggest that multitalker processing costs may be driven by both mechanisms operating over different time scales. Fully evaluating this claim requires a foundational understanding of both talker accommodation and auditory streaming; this article provides a primer on each literature and also reviews several studies that have observed multitalker processing costs. The review closes by underscoring a need for comprehensive theories of speech perception that better integrate auditory attention and by highlighting important considerations for future research in this area.

Keywords: Attention; Auditory streaming; Normalization; Speech perception; Talker variability.

Fig. 1

A schematic of the contextual tuning theory, which has been used to account for multitalker processing costs. On this view, whenever listeners detect that the current mapping between acoustics and phonetic categories is incomplete (e.g., when there is an overt change in talker), a resource-demanding mapping computation stage (gray box) is initiated. Because the direction of information flow in this model is dependent on the outcome of previous computations, this is an example of an active control model. Source: Magnuson (2018)