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“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.

Due to lack of a word/phrase/sentence boundary, summarization of Thai multiple documents has several challenges in unit segmentation, unit selection, duplication elimination, and evaluation dataset construction. In this article, we introduce Thai Elementary Discourse Units (TEDUs) and their derivatives, called Combined TEDUs (CTEDUs), and then present our three-stage method of Thai multi-document summarization, that is, unit segmentation, unit-graph formulation, and unit selection and summary generation. To examine performance of our proposed method, a number of experiments are conducted using 50 sets of Thai news articles with their manually constructed reference summaries. Based on measures of ROUGE-1, ROUGE-2, and ROUGE-SU4, the experimental results show that: (1) the TEDU-based summarization outperforms paragraph-based summarization; (2) our proposed graph-based TEDU weighting with importance-based selection achieves the best performance; and (3) unit duplication consideration and weight recalculation help improve summary quality.

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This study explored differences in CVV perception in two groups of Thai listeners: with normal hearing and with sensorineural hearing loss (with/without hearing aids). All participants chose one response in each of 210 Thai stimulus rhyming pairs, e.g., /taa/-/naa/. The rhyming monosyllabic words share an /aa/ vowel and mid tone, but differ in their initial phonemes (symmetrically distributed across 21 phonemes). While all stimuli for the normal hearing group were embedded in 4 signal-to-noise ratio levels, clean stimuli were presented to the patients. Comparisons of confusion patterns and perceptual distance were made. In both groups, /r/ is the most confusable phoneme, while /w/ is among the least. Perceptual representations of initial phonemes show five individual clusters: glide, glottal constriction, nasality, aspirated obstruent, and a combination of liquid and unaspirated obstruent. Patients' perceptual difficulty could be attributed to the nasality grouping, which is normally well separated, shifting closer to the glottal constrictions and aspirated obstruents. Hearing aids seem to improve perception of all phonemes by 10%, with /kh/ and /h/ showing the highest improvement rate, and /d/ the lowest. The instruments are beneficial in moving the nasality cluster further away from the nearby groupings.

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