Predictions from"speech banana"and audiograms: Assessment of hearing deficits in Thai hearing loss patients

Abstract

“Speech banana” is a banana-shaped plot of speech power distribution, where the abscissa and ordinate represent frequency and intensity. By superimposing speech banana over an audiogram, tested with pure tones, degrees of gain or loss of individual speech sound could be predicted. Speech banana has been constructed for English (Northern and Downs, 1984) and Swedish (Liden and Fant, 1954); however, none has been proposed for tonal languages, such as Thai. This work presents a construction of speech banana for Thai, a language with 21 consonants and 5 lexical tones. Specifically, intensity of each phoneme in the speech banana was calculated by differences of sound pressure level between the local maxima of power spectral density and equal loudness contour at 0 dB. Distribution of the 21 consonants is around 170-5700 Hz and 25-65 dB. Predictions of gain or loss of the phonemes from the constructed speech banana and audiograms were evaluated based on perception test results from seven Thai sensori-neural hearing loss patients, where they identified what they heard from a pair of rhyming words (210 stimuli) differing in initial phonemes, equally distributed across phonemes. Interestingly, the results showed high prediction rates of 71.4-85.7% for phonemes predominantly emphasized on frequency below 2000 Hz.

A multi‐objective approach for improving real‐time audio peak reduction and speech clarity in a dental drill noise reduction device

“…The optimal variable setting [0.1, 0.6, 0.6, −1] is applied to the AF algorithm to validate the optimisation outcome in the physical domain, with the noise reduction results obtained in the form PSD plot, shown in Figure 9 Details regarding quantified optimised responses are presented in Table 3. Fairly small differences between predicted and observed amongst the three responses suggest the adequacy of the regression models obtained in Equations ( 4)- (6). The regression model error is indicated by an 11% difference in the overall desirability D based on comparing experimental results to predicted values.…”

“…An intermediate shaft is driven by an electric motor normally operating at a speed of 150,000 revolutions per Minute (RPM) and the burr shaft (cutting tool) normally operates at a speed of 200,000 RPM. FIGURE 2 'Speech banana' frequency audiogram superimposed with a simplified frequency profile of an electric motor driven dental drill at no load (Adapted from Klingpornkun et.al [6])…”

“…The speech clarity objective, ObjS, can be represented by one response namely R S . Each of these three responses will have a unique regression model determined by RSM, see Equations ( 4)- (6). These regression models need to be minimised, maximised or optimised towards a specific target by finding a combination of appropriate variable settings.…”

“…A summary of the regression analysis can be found at the bottom of Appendix 1 and detailed regression results for each response, including the estimated term coefficients, can be found in Appendix 2. Second-order linear regression models derived from the results for the three responses are written as Equations ( 4) to (6). R P1 and R P2 represent the fitted response of objective peak reduction for the first and second noise peak, and R S represents the fitted response of objective speech clarity.…”

“…In an audiogram of sounds from the English language, speech phenomes cluster in a banana shape, which is often referred to as the ‘speech banana’ [5] as shown in Figure 2. It is broadly used in the assessment of hearing loss [5, 6] but can also indicate the frequency distribution of commonly used speech signals. From this figure, it can be observed that the majority of speech phenomes are within the range of 250 Hz to 5 kHz.…”