Analysis of Phonetic Balance in Standard English Passages

Abstract

Purpose A common way of eliciting speech from individuals is by using passages of written language that are intended to be read aloud. Read passages afford the opportunity for increased control over the phonetic properties of elicited speech, of which phonetic balance is an often-noted example. No comprehensive analysis of the phonetic balance of read passages has been reported in the literature. The present article provides a quantitative comparison of the phonetic balance of widely used passages in English. Method Assessment of phonetic balance is carried out by comparing the distribution of phonemes in several passages to distributions consistent with typical spoken English. Data regarding the distribution of phonemes in spoken American English are aggregated from the published literature and large speech corpora. Phoneme distributions are compared using Spearman rank order correlation coefficient to quantify similarities of phoneme counts in those sources. Results Correlations between phoneme distributions in read passages and aggregated material representative of spoken American English ranged from .70 to .89. Correlations between phoneme counts from all passages, literature sources, and corpus sources ranged from .55 to .99. All correlations were statistically significant at the Bonferroni-adjusted level. Conclusions Passages considered in the present work provide high, but not ideal, phonetic balance. Space exists for the creation of new passages that more closely match the phoneme distributions observed in spoken American English. The Caterpillar provided the best phonetic balance, but phoneme distributions in all considered materials were highly similar to each other.

Validation and cross‐linguistic adaptation of the Frenchay Dysarthria Assessment (FDA‐2) speech intelligibility tests: Hebrew version

“…To ensure phonetic balance of the word list, we rank ordered phonemes in both sources, following Lammert et al ( 2020 ) (removing the phoneme /ʔ/ that was not counted by Ben‐David & Bat‐El, 2016 ) (Table 3 ; and see Appendix A in the additional supporting information). The word list was found to match the norm for phonetic distribution in Hebrew, with a very high and significant Spearman rank order correlation, ρ = 0.862, p < 0.001 and a very high and significant Kandel's Tau, τ = 0.739, p < 0.001.…”

“…A Wilcoxon signed‐rank test, a two‐dependent samples non‐parametric paired test, was used to compare the English and Hebrew words in terms of length (number of syllables). Spearman rank order correlation and Kandel's Tau were used to determine the phonetic balance of the Hebrew word list (Lammert et al., 2020 ).…”

Using Dynamics of Eye Movements, Speech Articulation and Brain Activity to Predict and Track mTBI Screening Outcomes

“…The ImPACT test yields six component measures: verbal memory, visual memory, visual motor, reaction time, impulse control, and total symptom composites. A composite score is calculated from various relevant metrics within each component (44). The verbal memory composite includes word, symbol, and letter recall tests and provides an evaluation of attention, learning, and verbal memory.…”

“…The subject's vocal data was captured with an Audio-Technica ATM73A Fixed Charge Condenser head worn microphone. Vocal data included a read speech task called the Grandfather passage, which was developed to elicit a standardized phonetically balanced speech sample ( 45 ). Speech utterances had a mean duration of 51.5 s with standard deviation of 9.2 s. Each audio recording was transformed into three formant frequency trajectories, which represent the resonant frequencies of the vocal tract.…”

Assessing phoneme distribution for speech modeling

“…Furthermore, having simple but representative descriptions of running speech is becoming relevant for recent modeling efforts devoted to describing long term vocal ambulatory behaviors based on a collection of sustained phonemes [7]. The passages read offer the opportunity for the clinician to have control over the phonetic properties of the speech elicited, of which phonetic balance is an example often noted [8]. It also allows to control and monitor changes in some vocal parameters and vocal tract posture [9] .…”