Perceptual cues used by listeners to discriminate fluent from nonfluent narrative discourse.

BACKGROUND: Language fluency is a common diagnostic marker for discriminating among aphasia subtypes and improving clinical inference about site of lesion. Nevertheless, fluency remains a subjective construct that is vulnerable to a number of potential sources of variability, both between and within raters. Moreover, this variability is compounded by distinct neurological aetiologies that shape the characteristics of a narrative speech sample. Previous research on fluency has focused on characteristics of a particular patient population. Less is known about the ways that raters spontaneously weigh different perceptual cues when listening to narrative speech samples derived from a heterogeneous sample of brain-damaged adults. AIM: We examined the weighted contribution of a series of perceptual predictors that influence listeners' judgements of language fluency among a diverse sample of speakers. Our goal was to sample a range of narrative speech representing most fluent (i.e., healthy controls) to potentially least nonfluent (i.e., left inferior frontal lobe stroke). METHODS #ENTITYSTARTX00026; PROCEDURES: Three raters blind to patient diagnosis made forced choice judgements of fluency (i.e., fluent or nonfluent) for 61 pseudorandomly presented narrative speech samples elicited by the BDAE Cookie Theft picture. Samples were collected from a range of clinical populations, including patients with frontal and temporal lobe pathologies and non-brain-damaged speakers. We conducted a logistic regression analysis in which the dependent measure was the majority judgement of fluency for each speech sample (i.e., fluent or non-fluent). The statistical model contained five predictors: speech rate, syllable type token ratio, speech productivity, audible struggle, and filler ratio. OUTCOMES #ENTITYSTARTX00026;
RESULTS: This statistical model fit the data well, discriminating group membership (i.e., fluent or nonfluent) with 95.1% accuracy. The best step of the regression model included the following predictors: speech rate, speech productivity, and audible struggle. Listeners were sensitive to different weightings of these predictors.
CONCLUSIONS: A small combination of perceptual variables can strongly discriminate whether a listener will assign a judgement of fluent versus nonfluent. We discuss implications for these findings and identify areas of potential future research towards further specifying the construct of fluency among adults with acquired speech and language disorders.