Abstract
The Computer-Aided Speech Perception Assessment (CASPA) is a clinical measure of speech recognition that uses 10-item, isophonemic word lists to derive performance intensity (PI) functions for adult listeners. Because CASPA was developed for adults, the ability to obtain PI functions in children has not been evaluated directly. This study sought to evaluate PI functions for adults and four age groups of children with normal hearing to compare speech recognition as a function of age using CASPA. Comparisons between age groups for scoring by words and phonemes correct were made to determine the relative benefits of available scoring methods in CASPA.
Design: Speech recognition using CASPA was completed with 12 adults and four age groups of children (5-to 6-, 7-to 8-, 9-to 10-, and 11-to 12-yr olds), each with 12 participants. Results were scored by the percentage of words, phonemes, consonants, and vowels correct. All participants had normal hearing and age-appropriate speech production skills.
Results: Differences in speech recognition were significant across all age groups when responses were scored by the percentage of words correct. However, only differences between adults and the two youngest groups of children were significant when results were scored by the number of phonemes correct. Speech recognition scores decreased as a function of signal to noise ratio for both children and adults. However, the magnitude of degradation at poorer signal to noise ratios did not vary between adults and children, suggesting that mean differences could not be explained by interference from noise.
Obtaining PI functions in noise using CASPA is feasible with children as young as 5 yrs. Statistically significant differences in speech recognition were observed between adults and the two youngest age groups of children when scored by the percentage of words correct. When results were scored by the percentage of phonemes correct, however, the only significant difference was between the youngest group of children and the adults. These results suggest that phoneme scoring may help to minimize differences between recognition scores of adults and children because children may be more likely to provide responses that are phonemic approximations when words are outside their lexicon.
INTRODUCTION
Clinical speech recognition testing of children is complicated by factors beyond auditory sensitivity, including developmental differences in speech and language skills, cognition, memory, and attention. As a result, performance on speech recognition tasks tends to be more variable for children than that for adults. Clinicians attempt to limit such variability by using stimuli that are likely to be in a child's lexicon, such as in the Phonetically Balanced Kindergarten word lists (Haskins 1949) or a closed-set response task, such as the Word Intelligibility by Picture Identification test (Ross & Lerman 1970) . Even when test materials are specifically developed for children, individual differences in lexical knowledge have been shown to influence speech recognition (Sanderson-Leepa & Rintelmann 1976) . Additionally, the deleterious effects of noise on speech perception have been shown to be greater for young children than for adults (Nittrouer & Boothroyd 1990; Johnson 2000) . Some researchers have hypothesized that these age-related differences in performance are the result of a combination of sensory, cognitive, and linguistic factors (Elliot 1979; Hnath-Chisolm et al. 1998) ; that is, age-related differences in performance may confound the interpretation of clinical speech recognition results with children as they develop, particularly on tests that are developed for adult listeners.
Several studies have attempted to quantify the contribution of linguistic knowledge on speech recognition differences between children and adults. Using speech materials that varied in lexical, syntactic, and semantic predictability, Nittrouer and Boothroyd (1990) evaluated the recognition of consonantvowel-consonant (CVC) syllables and sentences for normalhearing children from 4 1 ⁄2 to 6 1 ⁄2 yrs of age in comparison with a group of young and older adults. Half of these stimuli were commonly occurring CVC words, whereas the other half were CVC nonsense syllables. Three types of sentences were used: zero probability sentences were both syntactically and semantically anomalous (e.g., "girls white car blink"); low-predictability sentences were syntactically appropriate, but semantically anomalous (e.g., "duck eats old tape"); and high-predictability sentences were syntactically and semantically appropriate (e.g., "most birds can fly"). By comparing performance across stimuli with differing levels of semantic and syntactic predictability, the relative contributions of each to speech recognition by children were evaluated. The results indicated that, for both young adults and children, the highest performance occurred when lexical, syntactic, and semantic cues were available. Overall, adults had better performance for sentences, words, and nonsense syllables in noise than the young children. However, when results were scored by the number of phonemes correct, intersubject variability decreased, and the differences in performance between adults and children also decreased. By analyzing the relative contributions of lexical, syntactic, and semantic information to overall performance, the investigators concluded that when children possess the appropriate linguistic knowledge, differences in speech recognition between children and adults in noise are minimal. -Chisolm et al. (1998) examined the speech perception abilities of children aged 5 to 10 yrs using auditory, visual, and auditory plus visual conditions to assess age-related changes in nonsense-syllable recognition. Nonsense syllables were chosen to minimize the influence of lexical knowledge on the task. Results indicated that children younger than 7 yrs had lower recognition scores than older children. The authors concluded that these differences may be related to differences in cognitive or linguistic skills between younger and older children. These findings suggest that, for a speech recognition task to provide useful information about the auditory capacity of children, the test should seek to minimize the influence of cognitive and linguistic demands. To accommodate the short attention spans of young children, the authors recommended that speech recognition tasks be kept brief.
Hnath
Although more time consuming to obtain than speech recognition at a single intensity level, Mackersie et al. (2001) suggested that performance intensity (PI) functions provide a more comprehensive estimation of speech recognition. PI functions can have a variety of clinical applications in situations where a measure of speech recognition at a single level is inadequate (Boothroyd 2008) . For comparison of aided and unaided speech recognition results with hearing aids or implantable devices, two PI functions are measured to determine the extent to which performance improves with their device. PI functions also may be useful to evaluate changes in hearing aid settings or in the assessment of speech perception for individual listeners over their dynamic range (Boothroyd 2008) . PI functions also have been used to detect retrocochlear hearing loss by testing for "rollover" effects at high intensities (Jerger & Jerger 1971) . However, because the variability of speech recognition is influenced by both the number of items correct and the number of stimuli (Thornton & Raffin 1978) , obtaining a PI function across a range of intensities requires a greater time commitment than the monosyllabic word list, single intensity-level approach.
Although it has been well documented that PI functions can provide a more comprehensive picture of speech recognition than the current clinical practice of word recognition at a single intensity (Sherbecoe & Studebaker 2002; Donaldson & Allen 2003) , PI functions are not often used clinically. According to a survey of 276 audiologists, 98% routinely include speech recognition tasks in hearing evaluations (Martin et al. 1994) . The most common speech recognition tasks are the speech reception threshold and monosyllabic word recognition in quiet. Given the time limitations in typical clinical settings, word recognition is frequently measured at a single level (typically 40 dB above the speech reception threshold). Although it is often assumed that this represents the maximum word recognition score (i.e., phonetically-balanced maximum), Kamm et al. (1983) reported that phonetically-balanced maximum estimates obtained at 40-dB sensation level reflected maximum word recognition in only 60% of their subjects. To obtain valid PI functions in less time, Boothroyd (2006) developed the Computer-Aided Speech Perception Assessment (CASPA). In this computer-based test, lists of 10 CVC words are presented over a range of intensity levels, and the tester enters subject responses into a computer. The software automatically scores results in terms of words, phonemes, consonants, and vowels correct and generates separate PI functions for each analysis. CASPA can be used to obtain a PI function in Ͻ5 min. Stimuli can be presented via earphones or in the sound field either in quiet or with competing noise.
Before the development of CASPA, phonemic scoring was much more time consuming than scoring based on the number of words correct. Phonemic scoring offers several advantages over scores based on the number of words correct (Markides 1978; Gelfand 1998) . Scoring based on phonemes correct increases the number of data points in CVC words by a factor of three, decreases variability, and improves the precision of interpreting small differences in performance across presentations (Gelfand 2003) . Boothroyd (2008) suggested that phonemic scoring might reduce the influence of differences in linguistic knowledge on speech recognition for young children when compared with adults because phonemic scoring is presumed to reflect better the ability of the listener to repeat acoustic phonetic cues instead of requiring recognition of the entire word. Some of the speech recognition differences between adults and children are considered to be related to differences in lexical knowledge. Phonemic scoring may minimize age-related differences in performance because children will likely use phonemic approximations of words that are not within their lexicon. Although several studies have evaluated the use of phonemic scoring with adults, few have studied the effects of this approach with children, and a comparison of word and phoneme scoring for the same group of listeners has not been evaluated directly in previous studies.
For CASPA, PI functions have been obtained for normalhearing adults under three conditions: binaural sound-field presentation in quiet, monaural headphone presentation in quiet, and monaural headphone presentation in noise (Boothroyd 2008) . At present, similar PI functions using the CASPA software have not been obtained for normal-hearing children. Because age-related differences in performance would be expected for children, such differences should be explored. The goals of this investigation were to (1) assess PI functions in noise for normal-hearing children in four age groups and for adults with normal hearing; (2) determine whether CASPA is a clinically feasible tool for the audiological assessment of young children; (3) identify the age at which normal-hearing children achieve speech recognition comparable with that of adults; and (4) compare differences in speech recognition between children and adults for the various scoring methods available in CASPA.
METHOD Participants
Forty-eight normal-hearing children between the ages of 5 yrs 0 mos and 12 yrs 7 mos with no history of speech and language concerns participated in this study. Twelve children (six boys and six girls) were recruited in each of the four age groups: 5-to 6-yr olds (mean age, 5 yrs 8 mos), 7-to 8-yr olds (mean age, 8 yrs), 9-to 10-yr olds (mean age, 9 yrs 11 mos), and 11-to 12-yr olds (mean age, 11 yrs 5 mos). All participants were required to pass a hearing screening at 15 dB HL bilaterally at octave frequencies from 250 to 8000 Hz. One 10-yr-old child was excluded from the study resulting from thresholds Ͼ15 dB HL in both ears. The Bankson-Bernthal Quick Screen of Phonology (BBQSP; Bankson & Bernthal 1990 ) is a norm-referenced screening tool that uses picture identification to elicit speech production with 28 color pictures of objects. The BBQSP was administered by the examiner, and children with significant production errors outside the normal range (Ϯ1 SD) for their age were excluded. One 8-yr-old child was excluded because of significant speech production errors on the BBQSP. The adult group comprised 12 normal-hearing adults (6 men and 6 women) between the ages of 24 and 37 yrs (mean age, 28.9 yrs). The native language of all participants was English. All participants were paid an hourly rate, and children were also provided with a toy and a book.
Stimuli
CASPA (Boothroyd 2006 ) comprises 20 lists with 10 CVC words per list. Each list is isophonemic, which means that it contains one instance of the same set of 30 phonemes (20 consonants and 10 vowels). The CASPA software allows the experimenter to control the presentation level. In the version of CASPA used for this study (Version 3.3), the stimuli are spoken by an adult female.
Instrumentation
The CASPA software was installed on a personal computer that controlled the stimulus level and recorded subject responses. Stimuli were generated by a Sound Max Digital PCI sound card and routed to a Sony SMS-1P powered monitor speaker. As recommended in the CASPA procedure manual (Boothroyd 2006) , the sound field was calibrated so that the noise level was 55 dB SPL measured at the position of the listener.
Procedure
Participants were tested in a sound-treated booth. Results were obtained in a single session of 30 to 45 min. Two examiners were present for each session. Participants were seated 2 ft from the loudspeaker and instructed to repeat the words presented. Children were instructed to repeat each CVC, and the examiner in the control room entered their responses into CASPA. In cases where a child was inattentive or vocalizing during stimulus presentation, the test item was repeated once. If a response was unclear, the two examiners conferred to make a decision or the child was asked to repeat his or her response. All subjects were encouraged to guess, if they were not sure of what they heard. Additionally, because one of the goals of the study was to examine the accuracy of phonemic scoring, all participants were told that some words are real and others are "made up," but each word should be repeated as heard, even if it did not sound like a word they knew.
The speech-shaped noise used in the CASPA software is spectrally matched to the long-term average speech spectrum of the talker. This noise was presented continuously at 55 dB SPL during all stimulus presentations. The level of speech varied systematically for each list, resulting in the signal to noise ratios (SNRs) shown in Table 1 . Although the specific lists presented to each subject were varied randomly without replacement by the software, the order of SNR was held constant across subjects. Examiners entered the participants' responses into the CASPA software via the keyboard, and after each trial, the software automatically scored the item based on the orthography. In cases where the examiner disagreed with the automated scoring, the examiner could override the score.
At the end of each test, the software scored the responses and analyzed the data in terms of percent correct words, phonemes, consonants (initial, final, and total), and vowels. Feedback as to the correctness of the response was not provided to the subjects. For some of the younger participants, a second examiner sat with the child to help maintain the child's attention but did not provide feedback during the task. Figure 1 displays PI functions in children and adults for the different scoring methods offered in CASPA, showing speech recognition performance (as mean percent correct) as a function of SNR for each of the four scoring approaches. In Figure  1 , the filled squares and hatched area represent the mean and range between the 5 and 95% confidence intervals for the adult listeners, respectively. The various open symbols represent children's mean performance by age group. Performance for all age groups was highest for vowels, followed by phonemes (consonants and vowels combined), consonants, and words. Note that the performance for words was more variable for each age group than that for phonemes. Percentage of words correct was less variable at higher SNRs, although there was almost no variability across age groups or scoring methods at the highest SNR. The individual variability of results for children compared with that in adults is plotted in Figures 2 and 3 , respectively. The filled circles in Figure 2 show individual percentage correct data for words as a function of SNR for the four age groups of children. Figure 3 shows similar data for phonemes as filled triangles. The gray shaded area represents the range of recognition scores for the adult group. Note that the performance for words was more variable for each age group than that for phonemes. Percentage of words correct was less variable at higher SNRs, although there was almost no variability across age groups or scoring methods at the highest SNR.
RESULTS
To address mean differences in performance across age group as a function of scoring approach, a multivariate analysis of variance (MANOVA) was completed with scoring method (word and phoneme) and SNR (Ϫ10, Ϫ5, 0, and 5 dB) as within-subjects factors and age group as a between-subject factor. Because word and phoneme scoring data were obtained from the same task, each score was analyzed as a separate dependent variable using a planned comparison. All percent correct speech recognition results were converted to rationalized arcsine units (Studebaker 1985) before statistical analyses to normalize covariance as a function of percent correct. Box's M analysis on rationalized arcsine units was not significant (Box's M ϭ 254.924, F[144,5508] ϭ 1.184, p ϭ 0.07), indicating that observed covariances did not differ significantly across conditions or groups and that the assumption of homogeneity of covariances was not violated. Although figures show results for vowel and consonant scoring, only word and phoneme scores were included in the MANOVA because these scoring methods would be of most utility in clinical applications of CASPA. Data at ϩ10 dB and ϩ15 dB SNR were excluded from the MANOVA because of consistent ceiling performance and lack of variance across all participants.
There was a significant effect of age group (5-to 6-, 7-to 8-, 9-to 10-, 11-to 12-yr olds, and adults) on the combined dependent variable of scoring method (F[8,108] ϭ 3.041, p ϭ 0.004, Wilks' ϭ 0.667, 2 p ϭ 0.184). Further analysis of each dependent variable (word and phoneme), using a Bonferroniadjusted ␣ level of 0.025 (0.05/2), indicated that significant mean differences existed between age groups for both word (F[4,55] ϭ 6.155, p Ͻ 0.001 2 p ϭ 0.309) and phoneme (F[4,55] ϭ 4.655, p ϭ 0.003, 2 p ϭ 0.253) scoring methods. Figure 4 compares results by scoring method as a function of age group for mean phoneme scores (black bars) and mean word scores (white bars). To determine which differences across age groups contributed to the significant effect for each scoring method, post hoc pairwise comparisons using Tukey's honestly significant difference were made with a calculated minimum mean difference of 5.792. Table 2 contains the mean and SD values for percent correct word and phoneme scores for each age group collapsed across SNR. Several patterns were observed based on the post hoc pairwise comparisons. When comparing the two scoring methods for each age group, phoneme scores were consistently higher than words scores for all age groups. Comparison of word scores across age groups revealed three different levels of performance. Adults had significantly higher word scores compared with those of all four groups of children. Differences in word scores among 7to 8-, 9-to 10-, and 11-to 12-yr olds were not significant, but all the three groups had higher word scores than the 5-to 6-yr olds. When pairwise comparisons were made for phoneme scores across age, only two distinct levels of performance emerged. Adults had significantly higher phoneme scores than 5-to 6-and 7-to 8-yr olds, but differences between adults and older children were not significant. When phoneme scores were compared across the age groups of children, the only significant difference in recognition was between 5-to 6-and 11-to 12-yr olds. Overall, this pattern of results suggests that phoneme scoring resulted in smaller differences in speech recognition scores between adults and children, but that adults still had significantly higher phoneme scores than the two youngest groups of children.
The interaction between age group and SNR was explored to determine whether the differences in speech recognition across SNR were consistent across age groups. The interaction between age group and SNR was not significant (F[12,165] ϭ 1.465, p ϭ 0.142, Wilks' ϭ 0.732, 2 p ϭ 0.096), suggesting that changes in speech recognition across SNR were consistent across age groups. Each age group showed a similar pattern of improved performance as SNR improved, as would be predicted from previous studies. The interaction between SNR and scoring method was also examined. There was a significant effect of SNR on the combined dependent variable of scoring method (F[6,328] ϭ 78.286, p Ͻ 0.001, Wilks' ϭ 0.169, 2 p ϭ 0.589). Analysis of each dependent variable (word and phoneme), using a Bonferroni-adjusted ␣ level of 0.025 (0.05/ 2), indicated that significant mean differences existed across SNR for both word (F[3,165] ϭ 179.996, p Ͻ 0.001, 2 p ϭ 0.766) and phoneme (F[3,165] ϭ 240.927, p Ͻ 0.001, 2 p ϭ 0.814) scoring methods. Post hoc analyses of the mean differences between scoring methods at each SNR were completed using Bonferroni-adjusted ␣ levels of 0.0125 (0.05/4). Mean differences between word and phoneme scores were significant, with phoneme scores significantly higher than word scores at each SNR. As the SNR increased, word recognition improved more rapidly and showed greater variability than phoneme scoring. Collapsed across all groups, mean phoneme scores ranged from 52 to 63% at the Ϫ10 dB SNR, whereas mean word scores ranged from 16 to 30% at the same SNR. However, at the highest SNR, percent correct scores for words and phonemes were essentially the same (92 to 99% for words and 97 to 99% for phonemes). These results reflect the robustness of phonemic cues, even at poor SNRs, where listeners are given credit for being able to identify some of the phonemes in a word when listening conditions are challenging.
DISCUSSION
The goals of this study were to (1) evaluate differences in PI functions obtained in noise between adults and children using CASPA, (2) determine whether CASPA is a feasible assessment tool to use with young children, (3) assess the age at which children reach adult-like performance on CASPA, and (4) compare word and phoneme performance between adult and child listeners. Overall, several trends emerged. Adults had significantly higher word recognition scores than all four age groups of children. The three older groups of children all had significantly better word scores than the 5to 6-yr-old age group. Therefore, word scoring on the CASPA resulted in significant differences in percent correct across age groups, both between adults and children and within different age groups of children. For phoneme scores, adults had significantly higher scores than the two youngest groups of children, but the differences between the adults and the two older groups of children were not significant. When comparisons were made for phoneme scores between age groups of children, the only significant difference was between the 11-to 12-and the 5-to 6-yr olds, with significantly better phoneme recognition scores for the older children. Scoring the results of the CASPA by phonemes correct results in fewer significant differences in performance across age groups but does not result in equivalent performance for all the age groups in this study.
The expected trend of decreasing performance and increasing variability in word and phoneme recognition also was observed as SNR decreased. In addition, the pattern of degradation in speech recognition scores as a function of decreasing SNR was similar for both children and adults in this study. As SNR decreased, phoneme recognition did not decrease as rapidly as word recognition, suggesting the preservation of some phoneme information even at the lowest SNRs. In comparing PI functions for adult and child listeners, all participants in this investigation showed a trend of increasing performance as SNR improved regardless of age or scoring method. However, when comparing mean word recognition for each of the child age groups with that of the adults, it is evident that the majority of children in the 5-to 6-and 7-to 8-yr old groups scored outside the range of performance for adult listeners at each SNR. For phoneme scoring, the variability in recognition decreased with age, and a larger number of children fell within or above the range of performance for adult listeners at each SNR. Of clinical importance is the fact that even with phoneme scoring, some children in each age group had PI functions that were outside the range of performance for adults, despite the fact that the average data were not significantly different when phoneme scoring was used. However, for those children who had PI functions outside of the range for adults, the differences were much less for phoneme scoring than for word scoring. Based on the current results, it seems that CASPA can be used reliably in a clinical setting to assess speech recognition for children as young as 5 yrs of age. However, future studies should attempt to obtain data from a larger sample of children so that normative PI functions can be developed for these age groups. Given the differences in word recognition scores observed between adults and children, CASPA adult normative data may overestimate scores for children in this age range. Although performance was not significantly different between the adult group and the two older groups of children when phonemic scoring was used, the 5-to 6and 7-to 8-yr olds had poorer mean phoneme recognition scores than adult listeners, suggesting that phoneme scoring does not eliminate age-related differences in speech recognition for this age group. These results are consistent with the findings of Hnath-Chisolm et al. (1998) , who reported age-related differences in the recognition of nonsense syllables for children younger than 7 yrs.
In this study, complete PI functions at six intensity levels were obtained for each participant in approximately 10 mins. All participants in this study were able to complete the task without difficulty. Given that many listeners in this study achieved maximum performance at SNRs of ϩ5 or ϩ10 dB, clinical implementation of CASPA could include fewer presentation levels or SNRs than were used in this study. In many cases, a valid PI function could be obtained using only three or four different intensity levels or SNRs depending on the point where the listener reaches maximum performance. For any test of speech recognition to be clinically useful for children, the test must also be able to be administered effectively in a short amount of time. PI functions in CASPA can be obtained with cooperative children in Ͻ10 mins.
The CASPA results for adults in this study are similar to those reported in a previous study of speech recognition of adults using CASPA (Mackersie et al. 2001 ). In the previous study, the mean percentage of phonemes correct for adults at 0 dB SNR was 74% compared with 79% in this study. Mackersie et al. did not evaluate speech recognition performance of their adult participants at the other SNRs used in this study, so additional comparisons between studies are not possible. Nittrouer and Boothroyd (1990) found that, at SNRs of 0 and 3 dB, children between 4 1 ⁄2 and 6 1 ⁄2 yrs had poorer performance for individual words and nonsense syllables than a group of young adults. When the current results for the 5-to 6-yr-old group at 0 dB SNR are compared with the results obtained at the same SNR for children of similar age in Nittrouer and Boothroyd's study, phoneme recognition is similar (64% in the current study versus 68% in the previous study). A statistical comparison of scoring method as a function of age was not included in the Nittrouer and Boothroyd study.
Effects of age and scoring method on speech recognition in this study suggest that the use of PI functions for adults and children is clinically feasible and may provide greater information than the assessment of word recognition at a single intensity. The ability to simultaneously obtain and compare words correct and phonemes correct offers clinicians a tool that can provide clinically relevant information in a relatively short period of time. Phoneme scoring helped to minimize age-related differences in recognition between adults and children as young as 9 yrs of age. Phoneme recognition at poorer SNRs was also less variable and did not decrease as rapidly as word recognition because some phonemic cues remain salient even at poor SNRs. Thus, phoneme scoring may provide a viable alternative for individuals with extremely poor word recognition, where clinicians wish to quantify residual auditory skills.

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