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160M. Blomgren, A.M. Goberman / Journal of Communication Disorders 41 (2008) 159–1781. IntroductionThe history of stuttering research is replete with examinations of the effects of variousmotor and linguistic complexity manipulations on the speech of people who stutter. Two ofthe most widely examined factors thought to influence frequency of stuttering are speechrate (e.g., Andrews & Harris, 1964; Boberg & Kully, 1985; Goldiamond, 1965; Guttmann,1893; Helps & Dalton, 1979; Howie & Woods, 1982; Ingham, 1975; Johnson & Rosen,1937; Smith & Kleinow, 2000; Starkweather, 1987; Vanryckeghem, Glessing, Brutten, &McAlindon, 1999) and utterance length (e.g., Brown & Moren, 1942; Brundage & Ratner,1989; Kleinow & Smith, 2000; Logan & Conture, 1995; Riley & Ingham, 2000; Silverman,1972; Soderberg, 1966; Tornick & Bloodstein, 1976; Wells, 1979; Wingate, 1967). Takentogether, the findings of these studies strongly support the notion that increased motor and/or linguistic speaking task complexity result in decreased speech fluency for stutteringspeakers. However, the interaction between various types of different speaking taskcomplexities are not well understood (Howell, Au-Yeung, & Pilgrim, 1999; Max &Gracco, 2005). The purpose of the present study was to examine the interaction (andrelative strengths) of a motor variable (speech rate stability) and a linguistic variable(length of utterance) on stuttering frequency.Considering the temporal aspects of speech production, there is general agreement thatif a stuttering speaker reduces his/her speech rate they will also reduce their stutteringfrequency (e.g., Andrews & Harris, 1964; Boberg, 1976; Boberg & Kully, 1985; Johnson &Rosen, 1937; Onslow, Costa, Andrews, Harrison, & Packman, 1996; Runyan & Runyan,1986; Webster, 1980). It has also traditionally been reported that increased speech rateusually results in increased stuttering (e.g., Bloodstein, 1944; Howell et al., 1999; Johnson& Rosen, 1937). For example, Kalinowski, Armson, Roland-Miezkowski, Stuart, andGracco (1993) reported that seven of their nine participants displayed increased stutteringduring speaking tasks involving a fast speaking rate compared to habitual rate tasks.However, Kalinowski, Armson, and Stuart (1995) later found no difference in stutteringfrequency between habitual and fast speaking rates, which led these researchers toconclude that increased speech rate does not determine stuttering frequency with the sameconsistency as does decreased speech rate. While it appears that the exact effect of changesin speaking rate on stuttering frequency may vary somewhat among stuttering speakers, itis generally accepted that changes in the temporal dynamics of speech do produce changesin stuttering frequency. These findings support suggestions that stuttering is, at least in part,a disorder of speech timing (Cooper & Allen, 1997; De Nil, Kroll, & Houle, 2001; Kent,1984; Van Riper, 1982).Extant research evaluating length of utterance has shown that stuttering is less likely tooccur on shorter utterances compared to longer utterances (e.g., Brown & Moren, 1942;Brundage & Ratner, 1989; Logan & Conture, 1995; Silverman, 1972; Soderberg, 1966;Wells, 1979; Wingate, 1967). For example, Tornick and Bloodstein (1976) found that lessstuttering occurred in the reading of short sentences (e.g., ‘‘She learned to swim.’’) whenthis sentence stood alone compared to when it was the initial part of a longer sentence (e.g.,‘‘She learned to swim in the clear water of the lake.’’). Logan and Conture (1995) have alsosupported the observation of less stuttering on short sentences in young children whostutter.
M. Blomgren, A.M. Goberman / Journal of Communication Disorders 41 (2008) 159–178 161Length of utterance may be considered a spatial variable in speech production as theamount of spatial planning (segmental as well as coarticulatory) required to produce aseries of phonetic targets increases from words to phrases to sentences. For example,production of a single CVC word such as ‘‘bat’’ requires advance spatial planning for threephonetic targets, while production of even a simple sentence such as ‘‘Susan picked up thebat’’ requires advance planning for approximately 16 phonetic targets (Perkell & Nelson,1982). Length of utterance may also be viewed as a linguistic variable, as longer utteranceswould typically be more syntactically complex than shorter ones. However, the influence oflength of utterance and syntactic complexity is not necessarily analogous. For instance,Ratner and Sih (1987) reported that fluency breakdown in both stuttering and nonstutteringchildren is more highly correlated with increases in syntactic complexity thanlength of utterance. Further, Kleinow and Smith (2000) reported that adult stutteringspeakers (during perceptually fluent speech) exhibited decreased speech motor stabilityduring production of more syntactically complex utterances but did not exhibit decreasedstability during simply longer utterances (controlled for syntactic complexity). While theeffects may at times be subtle, certain aspects of linguistic structure do appear to have clearmediating effects on stuttering frequency in both children and adults (Au-Yeung & Howell,1998; Brown, 1945; Wingate, 1988; Wolk, Blomgren, & Smith, 1998).As the realized motor sequelae of thought and language, speech is one of the mostcomplex movement sequences produced by humans. As such, it is practically impossible toexamine every single mediating variable. In order to examine complex movementsequences in non-speech motor routines, dynamic systems theories have been developed.The goal of dynamic systems theory is to use multidimensional models to understand thepatterns that arise from coordinated movement (Kelso, 1988). To form a multidimensionalmodel, the identification of relevant pattern variables in the complex system is crucial(Thelen, Kelso, & Fogel, 1987). Thelen (1988) states: ‘‘The first step toward a dynamicalmodel of any developmental phenomenon is understanding the behavior over time. Herewe face the nontrivial (and maybe even monumental) task of choosing the appropriatecollective variable: how best to capture the compression of the degrees of freedom.’’ (p.365). Therefore, in order to create a workable dynamical model of behavior, degrees offreedom must first be reduced.To properly reduce the degrees of freedom in an examination of speech production,relevant variables must first be chosen, and one method of identifying relevant variables isthrough examination of physiological and linguistic constraints (Kelso, 1995). Twofundamental constraints for fluent speech production are speech rate and length ofutterance (Howell et al., 1999; Kent & Read, 1992; Kleinow & Smith, 2000; Smith &Kleinow, 2000). Speech rate refers to the number of phonemes, syllables, or words per unitof time, while length of utterance refers to the number of phonemes, syllables, or wordsproduced in any particular utterance. Speech rate is temporally constrained because thereis a finite speed (both slow and fast) at which intelligible speech can reasonably beproduced, and length of utterance is constrained due to both respiratory demands (i.e.,breath groups) and to semantic/syntactic realities (e.g., the need for informationchunking).In summary, rate of speech and length of utterance may be thought of as the fundamentaltemporal/motor and spatial/linguistic variables in normal speech production, respectively.
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