The Phoneme Identification Test for Assessment of Spectral and Temporal Discrimination Skills in Children: Development, Normative Data, and Test–Retest Reliability Studies

Abstract

Background:

Previous research suggests that a proportion of children experiencing reading and listening difficulties may have an underlying primary deficit in the way that the central auditory nervous system analyses the perceptually important, rapidly varying, formant frequency components of speech.

Purpose:

The Phoneme Identification Test (PIT) was developed to investigate the ability of children to use spectro-temporal cues to perceptually categorize speech sounds based on their rapidly changing formant frequencies. The PIT uses an adaptive two-alternative forced-choice procedure whereby the participant identifies a synthesized consonant-vowel (CV) (/ba/ or /da/) syllable. CV syllables differed only in the second formant (F2) frequency along an 11-step continuum (between 0% and 100%—representing an ideal /ba/ and /da/, respectively). The CV syllables were presented in either quiet (PIT Q) or noise at a 0 dB signal-to-noise ratio (PIT N).

Research Design:

Development of the PIT stimuli and test protocols, and collection of normative and test–retest reliability data.

Study Sample:

Twelve adults (aged 23 yr 10 mo to 50 yr 9 mo, mean 32 yr 5 mo) and 137 typically developing, primary-school children (aged 6 yr 0 mo to 12 yr 4 mo, mean 9 yr 3 mo). There were 73 males and 76 females.

Data Collection and Analysis:

Data were collected using a touchscreen computer. Psychometric functions were automatically fit to individual data by the PIT software. Performance was determined by the width of the continuum for which responses were neither clearly /ba/ nor /da/ (referred to as the uncertainty region [UR]). A shallower psychometric function slope reflected greater uncertainty. Age effects were determined based on raw scores. Z scores were calculated to account for the effect of age on performance. Outliers, and individual data for which the confidence interval of the UR exceeded a maximum allowable value, were removed. Nonparametric tests were used as the data were skewed toward negative performance.

Results:

Across participants, the median value of the F2 range that resulted in uncertain responses was 33% in quiet and 40% in noise. There was a significant effect of age on the width of this UR (p < 0.00001) in both quiet and noise, with performance becoming adult like by age 9 on the PIT Q and age 10 on the PIT N. A skewed distribution toward negative performance occurred in both quiet (p = 0.01) and noise (p = 0.006). Median UR scores were significantly wider in noise than in quiet (T = 2041, p < 0.0000001). Performance (z scores) across the two tests was significantly correlated (r = 0.36, p = 0.000009). Test–retest z scores were significantly correlated in both quiet and noise (r = 0.4 and 0.37, respectively, p < 0.0001).

Conclusions:

The PIT normative data show that the ability to identify phonemes based on changes in formant transitions improves with age, and that some children in the general population have performance much worse than their age peers. In children, uncertainty increases when the stimuli are presented in noise. The test is suitable for use in planned studies in a clinical population.

Key Words

categorical perception - central auditory processing disorder - spectral processing - speech perception - temporal processing

This research is funded by the Australian Government through the Department of Health.

Portions of this research were presented at the Audiology Australia National Conference, Melbourne, Australia, May 20, 2016.

• REFERENCES

• American Academy of Audiology 2010 Clinical Practice Guidelines: Diagnosis, Treatment and Management of Children and Adults with Central Auditory Processing Disorder. http://audiology-web.s3.amazonaws.com/migrated/CAPDGuidelines8-2010.pdf_539952af956c79.73897613.pdf
• American Speech-Language-Hearing Association 2005 (Central) Auditory processing disorders [Technical Report]. www.asha.org/policy
• Blomert L, Mitterer H. 2004; The fragile nature of the speech-perception deficit in dyslexia: natural vs synthetic speech. Brain Lang 89 (01) 21-26
• Boersma P, Weenink D. 2014. Praat: Doing Phonetics by Computer (version 5.4.04). Amsterdam, The Netherlands: University of Amsterdam [Computer software];
• Boets B, Op De Beeck HP, Vandermosten M, Scott SK, Gillebert CR, Mantini D, Bulthé J, Sunaert S, Wouters J, Ghesquière P. 2013; Intact but less accessible phonetic representations in adults with dyslexia. Science 342 6163 1251-1254
• British Society of Audiology 2011. Position Statement: Auditory Processing Disorder (APD). Berkshire, UK: British Society of Audiology; 1-9
• Cameron S, Chong-White N, Mealings K, Beechey T, Dillon H, Young T. 2018; The Parsing Syllable Envelopes test for assessment of amplitude modulation discrimination skills in children: development, normative data, and test–retest reliability studies. J Am Acad Audiol 29 (02) 151-163
• Chang EF, Rieger JW, Johnson K, Berger MS, Barbaro NM, Knight RT. 2010; Categorical speech representation in human superior temporal gyrus. Nat Neurosci 13 (11) 1428-1432
• Dillon H, Cameron S, Glyde H, Wilson W, Tomlin D. 2012; An opinion on the assessment of people who may have an auditory processing disorder. J Am Acad Audiol 23 (02) 97-105
• Fant G. 1962. Formant Bandwidth Data. Speech Transmission Laboratory Quarterly Progress and Status Report. Stockholm, Sweden: Royal Institute of Technology; 1-2
• Goswami U. 2011; A temporal sampling framework for developmental dyslexia. Trends Cogn Sci 15 (01) 3-10
• Goswami U, Fosker T, Huss M, Mead N, Szucs D. 2011; Rise time and formant transition duration in the discrimination of speech sounds: the Ba-Wa distinction in developmental dyslexia. Dev Sci 14 (01) 34-43
• King WM, Lombardino LJ, Crandell CC, Leonard CM. 2003; Comorbid auditory processing disorder in developmental dyslexia. Ear Hear 24 (05) 448-456
• Koerner TK, Zhang Y, Nelson PB, Wang B, Zou H. 2016; Neural indices of phonemic discrimination and sentence-level speech intelligibility in quiet and noise: a mismatch negativity study. Hear Res 339: 40-49
• Liberman AM, Cooper FS, Shankweiler DP, Studdert-Kennedy M. 1967; Perception of the speech code. Psychol Rev 74 (06) 431-461
• Liebenthal E, Binder JR, Spitzer SM, Possing ET, Medler DA. 2005; Neural substrates of phonemic perception. Cereb Cortex 15 (10) 1621-1631
• MathWorks Inc 2014. MATLAB (Release 2014b) [computer software]. Natick, MA: The MathWorks Inc;
• McArthur G, Kohnen S, Larsen L, Jones K, Anandakumar T, Banales E, Castles A. 2013; Getting to grips with the heterogeneity of developmental dyslexia. Cogn Neuropsychol 30 (01) 1-24
• National Acoustic Laboratories 2000. Speech and Noise for Hearing Aid Evaluation. Sydney, NSW: National Acoustic Laboratories [CD];
• National Acoustic Laboratories 2015. NAL Position Statement on Auditory Processing Disorders. National Acoustic Laboratories; https://capd.nal.gov.au/capd-position-statement.shtml
• Phillips C, Pellathy T, Marantz A, Yellin E, Wexler K, Poeppel D, McGinnis M, Roberts T. 2000; Auditory cortex accesses phonological categories: an MEG mismatch study. J Cogn Neurosci 12 (06) 1038-1055
• Rosen S. 1992; Temporal information in speech: acoustic, auditory and linguistic aspects. Philos Trans R Soc Lond B Biol Sci 336 1278 367-373
• Serniclaes W, Sprenger-Charolles L, Carré R, Demonet JF. 2001; Perceptual discrimination of speech sounds in developmental dyslexia. J Speech Lang Hear Res 44 (02) 384-399
• Specht K. 2014; Neuronal basis of speech comprehension. Hear Res 307 (378) 121-135
• Stevens KN, Keyser SJ. 1989; Primary features and their enhancement in consonants. Language (Baltim) 65 (01) 81-106
• Vandermosten M, Boets B, Luts H, Poelmans H, Golestani N, Wouters J, Ghesquière P. 2010; Adults with dyslexia are impaired in categorizing speech and nonspeech sounds on the basis of temporal cues. Proc Natl Acad Sci USA 107 (23) 10389-10394
• Vandermosten M, Boets B, Luts H, Poelmans H, Wouters J, Ghesquière P. 2011; Impairments in speech and nonspeech sound categorization in children with dyslexia are driven by temporal processing difficulties. Res Dev Disabil 32 (02) 593-603
• Vermiglio AJ. 2014; On the clinical entity in audiology: (central) auditory processing and speech recognition in noise disorders. J Am Acad Audiol 25 (09) 904-917
• Vermiglio AJ. 2016; On diagnostic accuracy in audiology: central site of lesion and central auditory processing disorder studies. J Am Acad Audiol 27 (02) 141-156
• White-Schwoch T, Davies EC, Thompson EC, Woodruff Carr K, Nicol T, Bradlow AR, Kraus N. 2015; Auditory-neurophysiological responses to speech during early childhood: effects of background noise. Hear Res 328: 34-47
• Wilson WJ, Arnott W. 2013; Using different criteria to diagnose (central) auditory processing disorder: how big a difference does it make?. J Speech Lang Hear Res 56 (01) 63-70