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University of Georgia, Athens
University of California, Santa Barbara
Contact author: Roger J. Ingham, Department of Speech and Hearing Sciences, University of California, Santa Barbara, Santa Barbara, CA 93106. rjingham{at}speech.ucsb.edu
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Abstract |
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30–150 ms) in individuals who stutter, prompting the hypothesis that PIs in these individuals' speech are not distributed normally, particularly in the short PI ranges. It has also been hypothesized that this nonnormal PI distribution will be present during the stutter-free speech of affected persons. METHOD: A comparison was made between the distributions of PIs during oral reading by adolescent and adult speakers who stuttered (n = 13; 11 males) and by age- and gender-matched, normally fluent control participants.
RESULTS: The results did not support these hypotheses. The results showed that although there were significantly fewer PIs in the speech of the speakers who stuttered (probably because of their slower speaking rate), there was no significant difference between the PI distributions of both speaker groups. This was also true for comparisons between the stutter-free speech of the affected speakers and matched periods of speech produced by the control participants. The PI distributions from both groups were highly correlated.
CONCLUSION: The null hypothesis findings are discussed in relation to speech–motor- and neurologic-systems explanations for the fluency-inducing effects of reducing short PIs in the speech of individuals who stutter.
KEY WORDS: stuttering, phonation, stutter-free speech, adults
In a series of studies Ingham and colleagues (Gow & Ingham, 1992; Ingham et al., 2001; Ingham, Montgomery, & Ulliana, 1983) investigated the effect on stuttering of manipulating the frequency of occurrence of short phonated intervals (PIs; i.e., short voiced sounds produced by vocal fold vibration) during different speaking tasks. In general, the findings of those studies demonstrated that training individuals who stutter to speak with an approximately 50% reduction of 30–150-ms PIs (per minute of speaking time) results in substantial reductions in the frequency of stuttering. Furthermore, the resulting improvements in fluency do not necessarily involve concomitant increases in the frequency of longer duration PIs or a deterioration in speech naturalness (Gow & Ingham, 1992; Ingham et al., 2001). PIs in the 30–150-ms range represented approximately the lower 20th percentile of the distribution of PIs in the speech of affected speakers who participated in these studies.
Much of the research on PI modification has been prompted by efforts to identify the functional constituents of prolonged speech (Goldiamond, 1965; Ingham, 1984, Chapter 10), arguably the centerpiece of most current treatments for developmental stuttering in adolescents and adults (see Bothe et al., 2003). Less emphasis has been placed on investigating the extent to which stuttering is characterized by an abnormal distribution of PIs or whether training to modify specific PI frequencies results in speakers' achieving a normalized PI distribution.1 In this respect, PI variability is especially interesting because it is a speech–motor parameter that appears to exert functional control over stuttering. There are numerous speech–motor parameters within which differences between speakers who stutter and those who do not have been demonstrated (Bloodstein, 1995). However, relatively few of those differences (e.g., voice reaction time, short pauses) have been shown to be modifiable and exert direct functional control over stuttering (see Ingham, 1998); such a combination has clinical implications that have yet to emerge from investigations of differences in other speech–motor parameters. At the same time, researchers recognize that variations in the PI parameter likely involve concomitant variations in other speech–motor parameters.
Evidence that altering or modifying phonation will reduce stuttering has been derived mainly from studies conducted on the various so-called fluency-inducing procedures. For example, in a number of studies, researchers have shown that masking (Andrews, Howie, Dozsa, & Guitar, 1982), chorus reading (Andrews et al., 1982), singing (Colcord & Adams, 1979), rhythmic speech (Brayton & Conture, 1978) and, of course, prolonged speech (Goldiamond, 1965, 1967; Packman, Onslow, & van Doorn, 1994) may involve alterations in the proportions of phonated speech. The pattern of changed phonation is not consistent across these fluency-inducing procedures (see Ingham, 1990), but when this evidence of phonation change is related to the consistent finding that stuttering speakers are relatively slow to initiate and terminate phonation (see Bloodstein, 1995), it strongly suggests that phonation management or control is functionally related to stuttering.2
The apparent interaction between phonatory behavior and stuttering may also be related to evidence that adult speakers who stutter tend to display relatively longer intervals of phonation during stutter-free speech (Pindzola, 1987; Prosek & Runyan, 1982), a characteristic that is less evident in children who stutter (Healey & Adams, 1981; Zebrowski, Conture, & Cudahy, 1985). Onslow and Ingham (1987) speculated that this age effect may occur because older speakers have learned to manage their disorder, however imperfectly, by using slower voice initiation and longer intervals of phonation. Note, however, that research on phonation during affected speakers' stutter-free speech has been focused on extended rather than shortened intervals of phonation. Nonetheless, these findings highlight the importance of determining the extent to which PI production during the stuttered and stutter-free speech of affected individuals is significantly different than that that occurs during normally fluent speech production.
An equally important reason for learning the relationship between PI production in person who stutter and normally fluent speakers is that improved fluency produced by modifying the frequency of short-duration PIs may be associated with important neural changes in stuttering speakers. Ingham, Ingham, Finn, and Fox (2003) reported the results of an investigation using positron emission tomography (PET) with 17 adult speakers who stuttered (9 men), who had completed part of the Modified Phonation Interval (MPI; Ingham et al., 2001) therapy program, which trains for reduced PI production in the lower 20th-percentile-ms range of each speaker's base-rate PI distribution. The treatment was sufficient to enable the participants to be scanned while producing essentially stutter-free and natural sounding speech during brief monologues. A comparison with normally fluent controls showed that the stuttering speakers displayed normalized cerebral blood flow (CBF) activations in most regions that have been associated with normal speech production (Indefrey & Levelt, 2004; Jürgens, 2002). By contrast, the relatively normal fluency produced by adult speakers who stutter (n = 20; 10 men) during chorus reading was associated with CBF activations in significantly fewer of these same neural regions. This was not a straightforward comparison (monologues were compared with oral reading), but the findings suggest that, compared with chorus reading, the fluency induced during short-duration PI reduction activated neural regions that support normal speech. It is not clear, however, if MPI training resulted in the participants who stuttered achieving a normal pattern of PI production and thereby normalized neural changes, or if the normalized neural changes achieved by the MPI training required a nonnormal distribution of PIs. That information can only be obtained by comparing the distributions of PIs produced by normally fluent speakers and speakers who stutter, which was the purpose of the present study.
For our primary hypothesis in this study, we proposed that the distribution of PIs (within the 30–1,000-ms range) during oral reading by adult and adolescent speakers who stuttered would be significantly different from that found during oral reading by age- and gender-matched, normally fluent controls. To test whether an abnormal distribution of PIs in speakers who stuttered was a result of the presence of abnormal phonatory gestures produced during occasions of stuttering (Conture et al., 1977, 1985; Shapiro & De Cicco, 1982), we tested a secondary hypothesis that the distribution of 30–1,000-ms PIs during the stutter-free speech of affected speakers (i.e., when recorded intervals of speech containing stuttering were removed) would be significantly different from the distribution of 30–1,000-ms PIs during equivalently adjusted speech samples from normally fluent speakers. Support for this hypothesis would be consistent with evidence of unusual characteristics in the stutter-free speech of affected speakers (Finn & Ingham, 1989; Pindzola, 1987; Prosek & Runyan, 1982).
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Setting and Apparatus
During the experiment, participants sat alone in a sound-controlled room where their PIs were recorded using the MPI software program system. As described in Ingham et al. (2001), the MPI program uses customized software and is operated using Windows XP on a desktop personal computer. The MPI program uses two other hardware items: an accelerometer (ACH-01-04) and a customized preamplifier unit for conditioning the accelerometer signal. The accelerometer is a piezo-electronic transducer with no sensitivity relative to earth and a 2-Hz to 20-kHz frequency response. It is housed within a Velcro neckband and is wired to the preamplifier, which is, in turn, connected to the computer. Vocal fold vibration is registered by the accelerometer, which is positioned within the neckband so that its surface is just below the thyroid prominence. The accelerometer signal is routed to a bandpass (80–300 Hz) filter and to the preamplifier unit. The MPI system uses a Sound Blaster Live card (16-bit sampling) with a 10–44-kHz frequency response. The system sets the digitization rate for the sound card at 12 kHz and ignores 11/12 samples, providing an effective sampling rate of 1 kHz. The input signals are integrated and smoothed over a 10-ms window after which only alternate values are retained, thereby producing a 500-Hz sampling rate for the conditioned signal. Before each MPI session, the system noise floor is established. When the intensity of the input signal exceeds 10% above the noise floor, a PI signal is initiated, and it ceases when the signal recedes below the noise floor.
Before the start of each participant's experiment, preliminary steps established that the accelerometer signal during speech met prescribed intensity level criteria and that the system's noise floor when the participant was silent was within prescribed limits (typically between 62.5 and 75 mV). A presession PI recording accuracy test was also made with each participant using an oral counting task. This test requires the MPI system to record a consistent and predetermined number of PIs for the oral counting task. Last, each participant produced a series of voiceless utterances and swallowing and head movements consistent with normal utterances, each for 1 min, to ensure that the settings did not produce a false PI signal. During 3-min oral reading trials, the MPI system recorded all PIs using that same setting.
All participants were audiovisually recorded during each session using a digital video camera (Canon ZR 70) with shotgun microphone; the session recordings were stored in a retrievable digital format. All PI signals were stored in a temporal format that made it possible to retrieve all PIs in the 30–1,000-ms range. PIs 
30 ms were excluded from analysis because tests conducted during previous studies (Gow & Ingham, 1992; Ingham et al., 2001) showed that PIs in this range are consistently confounded with head movement and swallowing. Only a few PIs from both groups exceeded 1,000 ms in the present study.3
Procedure
During the experiment, each participant sat alone in the sound-treated room in front of a table with a computer monitor and keyboard. Participants could choose to read from one of three books (Kagan, 1998; Linnea, 1995; Ridley, 1999) selected for general text homogeneity by being free of conversational content. Each participant completed three 3-min oral reading tasks, each separated by a 1-min data storage period. Participants were prompted to start and stop reading by tones emitted by the MPI computer program. During each reading task, a research assistant who was trained on the Stuttering Measurement System (SMS; Ingham, Bakker, Ingham, Kilgo, & Moglia, 1999) and Stuttering Measurement and Assessment Training (SMAAT; Ingham et al., 1998) programs recorded all syllables, stuttered and nonstuttered, using the computer mouse with the SMS system (SMS is built into the MPI program). The mouse-button–counted syllables were then converted into percentage SS and stutter-free syllables per minute (SFSPM). In addition, the MPI program recorded the number and percentage of 5-SI in which the judge recorded (from a mouse-button press) one or more stuttered events, including their duration.
The following procedure was used for the analysis of the data. For each participant, the millisecond duration of all PIs in each of the three 3-min oral readings was derived using the MPI program software. This software also superimposed a 5-SI template over each 3-min oral reading record. The PI data were then collated within Microsoft Excel files. The 5-SI in each participant's oral reading that contained a complete or partial stuttered event (identified from the mouse-button-press record) were then able to be isolated within the file. This made it possible to quantify each 3-min trial both with and without stuttered intervals. Each affected participant's 3-min trial PI record was then aligned with their age-matched control participant's PI record so that corresponding 5-SI could be removed from both PI records to test for differences between the groups when stuttered intervals were removed.
The three 3-min oral readings from each participant were collapsed into one data set so that all PIs could be ranked from the shortest to the longest duration. The 30–1,000-ms range was then subdivided into 50-ms ranges (except for the 30–50-ms range, which was left as a 20-ms subdivision) and the total number of PIs from each participant that occurred within each of these 20 subdivisions provided the raw data. These raw data were also converted into the percentage of each participant's total PIs within each of the 20 subdivisions. This conversion was necessary to control for the effect of different speaking rates within and between the speaker groups (see the Results section). The mean total number of PIs and percentage of total PIs within each of the 20 subdivisions were calculated for the following criteria: (a) the entire 9-min of the affected speakers' speech, (b) the matched control participants' speech, (c) the stutter-free speech of the speakers who stuttered, and (d) the matched control participants' speech minus those segments "matching stuttered intervals."
Calibration and Reliability
The accuracy of the MPI system's measurements of the duration of PIs was determined by comparing MPI-recorded PI durations with PI durations identified using an acoustic analysis program. These durations were obtained during two independent tests: (a) a trial test using a randomly selected recording of a participant's 3-min oral reading trial and (b) a nontrial test using oral reading of a phonetically balanced series of three-word combinations. The trial-test intervals were the first 50 PIs recorded during an affected speaker's trial. They ranged from 18 ms to 870 ms. The PI durations, as measured by the MPI program, were then compared with the durations derived from the trial's acoustic recording using the PRAAT (version 4.2.07) acoustic analysis program (Boersma & Weenink, 2003). During the nontrial test, the MPI system measured PI durations while a normally fluent speaker was recorded producing a series of three-word utterances, each separated by approximately 5-s silent periods. The first 50 PI intervals recorded and measured by the MPI program were selected from this task for comparison with the durations derived from the PRAAT, as described above. They ranged from 198 ms to 624 ms. For the trial test, the 40/50 intervals measured by the MPI system and by PRAAT differed by 20 ms or less (five differed by 23–28 ms; five by 31–37 ms). For the nontrial test, 41/50 intervals measured by both systems differed by 20 ms or less (two differed by 22–23 ms; seven by 34–40 ms). The similarity between PI measurement accuracy obtained during both test conditions indicates that the MPI program's phonation-interval measures possessed satisfactory accuracy, irrespective of speaking task.
Interjudge reliability of stuttering judgments was assessed by a research assistant trained on the SMS and SMAAT programs and who was unfamiliar with the study. Using the SMS program, this assistant rated one of each affected speaker's 3-min audio-visually recorded speaking tasks. Interjudge and intrajudge agreement are displayed in Figure 1. The figure shows that the percentage of agreed stuttered intervals by the experimenter and research assistant ranged between 75% and 97% across the speakers. The experimenter's intrajudge reliability was calculated by arranging a "blinded" rerating of the same set of speaking task recordings at least 3 months after the experimental trials were completed. Intrajudge agreement for stuttered intervals ranged from 81% to 100% across the speakers. It should be noted that this exacting agreement metric was not inflated by the inclusion of the much higher number of nonstuttered intervals.
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Results |
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We conducted log-linear chi-square analyses (Wickens, 1989) on the PI frequencies within the 20-PI-range subdivisions from the 26 participants (ns = 13 per group), that is, a total of 520 data cells. Wickens (1989) cautioned that chi-square tests require that "separate observations are probabilistically independent" (pp. 27–28), implying that each data cell observation (a PI) should derive from a separate participant (subject). He also noted, however, that a "series of trials from a single subject sometimes can be treated as independent events" (pp. 27–28), as in the case of psychophysical data. Thus, PIs were conceptualized as independent events because there is no evidence that the occurrence of a particular PI will modify the probability of occurrence of a different duration PI.
To test whether the relative frequency of responses in different subdivisions changes across groups, the relative subdivision changes were treated as estimates of the population distribution of such frequencies for a group or for an individual. Ideally, two hypotheses should be tested: H1 = the distribution functions for group participants are identical, and H2 (which assumes H1 is true) = the distribution functions for the two groups are identical. However, absent such a test procedure, log-linear independence tests (see Wickens, 1989, pp. 33–36) were applied to tests for dependence between groups and subdivisions (frequencies in different PI ranges) and the dependence of participant effects and subdivision frequencies. Unfortunately, the theory for such an analysis does not help determine the dependence of participants nested within groups with other variables; indirect evidence about these effects must be obtained.
Another reason for performing the log-linear independence tests described by Wickens (1989) is that traditional chi-square tests are not applicable to three-dimensional or higher dimensional data structures. Thus, Wickens wrote, "One can extend the analysis to more than two classifications. The respondents in [a] poll can be classified by sex as well as region and opinion, to make a 4 x 2 x 3 table. Analyzing this multiway table allows more complicated relationships to be revealed" (p. 2). Wickens also noted objections to collapsing a three- or more-way table to a group of two-way tables, concluding, "All this is enough to demand a set of higher-order analysis techniques and an organized set of procedures to employ them" (p. 2). Log-linear analyses or, more generally, multiway contingency-tables analysis, are those techniques.
Complete Data Set Analysis
For the complete data set, which included stuttered intervals, we would have liked to segregate Participant x Subdivision effects within groups and Group x Subdivision effects within an overall analysis. Because we could not find a way to perform this segregation, we performed one log-linear analysis to assess Group x Subdivision independence, yielding a chi square of 86.83 (p < .0001, N = 519, df = 19). In the absence of Participant x Subdivision effects, this suggests that group membership produced a different distribution of relative PI frequencies in the different PI ranges. An additional log-linear analysis of the speakers who stuttered yielded a Participant x Subdivision independence chi square of 2,258.16 (p < .0001, N = 259, df = 228). In addition, a log-linear analysis of control participants yielded a Participant x Subdivision independence chi square of 3,123.63 (p < .0001, N = 260, df = 228). This indicates that, regardless of group membership, participants in any given group had different distributions of relative PI frequencies in different subdivisions. Hence, the log-linear analyses suggest that the Group x Subdivision interaction is likely an artifactual difference that is due to Participant x Subdivision effects. It is not clear how much residual effect of Group x Subdivision interaction remains.
Stutter-Free Intervals Analysis
For the data set excluding stuttered intervals, a log-linear analysis yielded a Group x Subdivision independence chi square of 34.05 (p < .0001, N = 494, df = 19). This suggests that group membership produced a different distribution of relative frequencies in different PI ranges. However, as before, the averaged Participant x Subdivision relative frequencies could account for some or all of the Group x Subdivision effect. An additional log-linear analysis of speakers who stuttered yielded a Participant x Subdivision independence chi square of 5,246.27 (p < .0001, N = 245, df = 225). In addition, a log-linear analysis of control participants yielded a Participant x Subdivision independence chi square of 4,048.31 (p < .0001, N = 249, df = 227). This indicates that regardless of group membership, participants in any given group had different distributions of relative PI frequencies in different subdivisions. Hence, the log-linear analyses suggest that, at least in part, the Group x Subdivision interactions reflect artifactual differences that are due to Participant x Subdivision effects. Again, it is not clear how much residual effect of Group x Subdivision interactions remains.
A second set of analyses involved tests of group effects on PI responses within each subdivision. Using t tests, we compared the relative PI frequencies within each PI range for the two groups, using each individual's relative frequency (proportion per subdivision) and computing a mean for each participant. In all t tests, we used a two-tailed procedure and assumed unequal variance. For the data set that included both stuttered and nonstuttered intervals (see Figure 2), the 20 t tests for different PI ranges were inspected using a Bonferroni correction. 4 This required significant effects to be at the .0026 level or beyond. The nearest significant effect was .041. For the data set that included only nonstuttered intervals (see Figure 3), the 20 t tests were again inspected using a Bonferroni correction, where significance would be obtained only at .0026 or less. The nearest significance level was .024.
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Discussion |
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It should be recognized, of course, that in this study, the speakers who stuttered and the control participants differed with respect to speech rate and total number of PIs that they produced. The affected speakers, as expected, read aloud more slowly than the control participants, but they were also relatively slower during their stutter-free speech. As a result, the affected speakers' total PIs in all PI ranges were significantly less than the control speakers' PIs, although this was true only when PI frequencies were derived with stuttering present (the lower PI frequencies when stuttering was absent did not reach significance). In other words, the speakers who stuttered would be expected to produce relatively fewer short-duration PIs (13%) simply because they produced fewer PIs overall. In this regard, the cohort of experimental participants in the present study was not unusual in speaking at slower than normal speaking rates (see Bloodstein, 1944). Previous researchers have shown that when intervals of speech containing stuttering are excluded from analysis, speakers who stutter may still display a perceptually tenuous fluency (Young, 1984) that may be characterized by unusual pauses (Love & Jeffress, 1971) and other unusual articulatory features (Di Simoni, 1974). These unusual features are most likely learned adjustments to chronic stuttering because they do not characterize the speech of young children who stutter (Colcord & Gregory, 1987; Krikorian & Runyan, 1983). Although these adjustments might be learned, it does not imply that the entire speech production system is maladjusted. This seems to be evident from an investigation of speech rate in adult speakers who stuttered by Smith and Kleinow (2000). They found that even when speech rate was manipulated, speakers who stuttered "can speak fluently at habitual and fast rates with articulatory kinematic parameters overlapping those of normally fluent speakers" (p. 534).
One interesting finding from the present study concerned the effect on the PI distribution of removing stuttered speech intervals from the affected speakers' samples. The very high correlation between the PI distributions of the complete speech samples and the stutter-free samples (r = .997) suggests that occasions of stuttering do not interact with the frequency of PIs in particular duration ranges. The present finding replicates a similar effect obtained in an unpublished investigation of the MPI by Gow (1998). At the least, this suggests that either the phonatory gestures that typify stuttering events (Conture, McCall, & Brewer, 1977; Conture, Schwartz, & Brewer, 1985; Shapiro & De Cicco, 1982) do not have abnormal features that alter the distribution of PIs within affected speakers' speech patterns or that the phonatory gestures are simply randomly distributed across the PI duration ranges.
The results of the present study imply that a disproportionate production of short-duration PIs is not part of the core problem in stuttering. However, it is possible that it is not the frequency of short-duration PIs that is problematic, but the affected speakers' ability to manage the production of short-duration PIs. The dominant theory behind the search for any impairment in affected speakers' speech production skills has been various versions of a speech–motor-deficit model (e.g., Zimmermann, 1980). It is not clear, however, that such peripherally focused models have advanced the understanding of the source of such a deficit (see Ingham, 1998). For that reason, there may be more to gain from a search that is framed by the nascent neural system models of speech production (Indefrey & Levelt, 2004; Jürgens, 2002). From that perspective, the present findings might be more productively interpreted by relating them to the findings of neuroimaging studies on speakers who stutter and normally fluent speakers.
The present findings may be related to those obtained by Ingham et al. (2003) in a PET study conducted with 17 adult speakers who stuttered (9 men) undergoing MPI treatment. One feature of the findings was the interesting role of putamen.5 It is gradually becoming apparent from brain imaging research that putamen and specific regions of cerebellum either interact, or are involved in, managing the production of syllables of varying duration and rate (Jeffries, Fritz, & Braun, 2003; Riecker, Wildgruber, Dogil, Grodd, & Ackermann, 2002; Sidtis, Strother, & Rottenberg, 2003; Vanlancker-Sidtis, McIntosh, & Grafton, 2003; Wildgruber, Ackermann, & Grodd, 2001). In addition, researchers have found that during stuttered speech, the basal ganglia in general (Brown, Ingham, Ingham, Laird, & Fox, 2005) and right putamen in particular are abnormally inactive (Fox et al., 1996; Ingham et al., 2003). However, after receiving partial MPI treatment, when the participants' frequency of short PIs was reduced, right putamen was activated (Ingham et al., 2003). Activation in putamen was also reported during monologue tasks by 4 individuals who had recovered from stuttering (without formal treatment) after adolescence (see Ingham et al., 2003). These findings are particularly interesting when related to a recent PET study by Wildgruber et al. (2001). They found that when normally fluent speakers repeated a short phonated syllable (/ta/) at a rate of 4.0–5.5/s, there were consistent cerebellum and putamen activations. However, while repeating that syllable at a much slower rate (2.5–4.0/s), cerebellum was inactive, but putamen continued to be active. It would be expected that at the faster rate, the syllable required much shorter PIs than at the slower rate.6 The reduced frequency of short PIs during post–MPI-treatment, stutter-free monologues in the Ingham et al. (2003) study would almost certainly have involved reduced PIs produced at a rate of 4.0–5.5/s but would have allowed for PIs to be produced at the rate of 2.5–4.0/s. In other words, although speakers who stutter may not engage putamen activity (as do normally fluent speakers) during their stuttered speech production, putamen activations do occur when their short PIs and stuttering are reduced. The obvious implication is that improved fluency in speakers who stutter may be obtained by reducing the use of short PIs that consistently fail to activate a neural region, putamen, which is necessary for normally fluent speech.
The present study strongly suggests that the PI distribution in people who stutter is not different from that of people who do not stutter. This has important implications for future research into the treatment and cause of this disorder. The findings tend to suggest that the production of phonation per se is not abnormal in speakers who stutter, a finding that is consistent with the results of many investigations of speech–motor activity among speakers who stutter (Ingham, 1998). We speculate that the part of the source of difficulty in managing this particular speech–motor activity in a way necessary for fluent speech production will be located in a neural system dysfunction related to basal ganglia.
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Acknowledgments |
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Footnotes |
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2 It is of interest that among other putative, prolonged speech parameters (e.g., "soft contacts," "gentle onsets," or "easy onsets"), only acoustic rise time—an approximation to gentle onsets—has been investigated during the speech of speakers who stutter (Borden, Baer, & Kenney, 1985; Peters, Boves, & van Dielen, 1986). No study has reported on the effect on stuttering of modifying acoustic rise time.
3 The decision to use 50 ms was somewhat, but not completely, arbitrary. At the extremes, we could have used a 1-ms range or a much larger range (e.g., 250 ms). The 50-ms unit was selected because it had been used in initial investigations (Ingham, Montgomery, & Ulliana, 1983) and would subdivide the PI range found to be functional in previous studies (30–150 ms; Gow & Ingham, 1992; Ingham et al., 2001). The ceiling of 1,000 ms was a post hoc decision that was based on the low frequency of PIs exceeding that size. Two participants from each group displayed zero data in 50-ms boxes beyond 1,000 ms, and only 15 PIs, evenly distributed across both groups, exceeded 1,000 ms.
4 A Bonferroni inequality correction for each set of 20 t tests is justified for the following reasons. For a given 
criterion of significance for any single t test, the binomial theorem probability based on 20 independent tests of no significant results is (1 – )20. For dependent tests, an inequality applies, yielding 1 – (1 – )20 as the maximum possible probability of one or more significant results. Therefore, given .05 = 1 – (1 – )20, then = .0026. There is, however, a special issue here: Although we assumed the number of PIs in one subdivision to be independent of the number in any other PI range, an analysis using proportions in each PI range was constrained, with the total proportion being 1.0. Because the Bonferroni inequality takes an independent binomial result as its upper bound, the ipsative nature of these data does not invalidate the present conclusions.
5 A recent review by Alm (2004) also suggested that this part of the basal ganglia may have an important role in stuttering. Alm's review, which draws attention to the role of putamen in stuttering, is derived from an analysis of only a portion of the current brain imaging research on stuttering. This may explain why the review overlooks the role of some important cortical regions that might interact with putamen and likely have equivalent prominence in any neural system that ultimately emerges as functionally related to stuttering. Foremost among these regions would be those in the temporal lobe that have been described by Braun et al. (1997), Fox et al. (2000), and Ingham et al. (2004) as displaying CBF activity that is negatively correlated with stuttering frequency.
6 A series of trials were conducted in the second author's research laboratory at the University of California, Santa Barbara, using the procedure described by Wildgruber et al. (2001) and the MPI program. Wildgruber et al. showed that all PIs produced by 2 male and 3 female, normally fluent adults when uttering /ta/ at the faster rate (4.0–5.5/s) for 5 s were less than 100 ms, but at the slower rate (2.5–4.0/s for 5 s) they consistently exceeded 150 ms.
Received December 27, 2004
Accepted April 3, 2005
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