Five speech-language clinicians and 5 naive listeners rated the similarity of pairs of normal and dysphonic voices. Multidimensional scaling was used to determine the voice characteristics that were perceptually important for each voice set and listener group. Solution spaces were compared to determine if clinical experience affects perceptual strategies. Naive and expert listeners attended to different aspects of voice quality when judging the similarity of voices, for both normal and pathological voices. All naive listeners used similar perceptual strategies; however, individual clinicians differed substantially in the parameters they considered important when judging similarity. These differences were large enough to suggest that care must be taken when using data averaged across clinicians, because averaging obscures important aspects of an individual’s perceptual behavior.

Sixteen listeners (10 expert, 6 naive) judged the dissimilarity of pairs of voices drawn from pathological and normal populations. Separate nonmetric multidimensional scaling solutions were calculated for each listener and voice set. The correlations between individual listeners' dissimilarity ratings were low However, scaling solutions indicated that each subject judged the voices in a reliable, meaningful way. Listeners differed more from one another in their judgments of the pathological voices (which varied widely on a number of acoustic parameters) than they did for the normal voices (which formed a much more homogeneous set acoustically). The acoustic features listeners used to judge dissimilarity were predictable from the characteristics of the stimulus sets' only parameters that showed substantial variability were perceptually salient across listeners. These results are consistent with prototype models of voice perception They suggest that traditional means of assessing listener reliability n voice perception tasks may not be appropriate, and highlight the importance of using explicit comparisons between stimuli when studying voice quality perception

Sixteen listeners (10 expert, 6 naive) judged the dissimilarity of pairs of voices drawn from pathological and normal populations. Separate nonmetric multidimensional scaling solutions were calculated for each listener and voice set. The correlations between individual listeners’ dissimilarity ratings were low However, scaling solutions indicated that each subject judged the voices in a reliable, meaningful way. Listeners differed more from one another in their judgments of the pathological voices (which varied widely on a number of acoustic parameters) than they did for the normal voices (which formed a much more homogeneous set acoustically). The acoustic features listeners used to judge dissimilarity were predictable from the characteristics of the stimulus sets’ only parameters that showed substantial variability were perceptually salient across listeners. These results are consistent with prototype models of voice perception They suggest that traditional means of assessing listener reliability n voice perception tasks may not be appropriate, and highlight the importance of using explicit comparisons between stimuli when studying voice quality perception

SRI International’s EduSpeak® system is a software development toolkit that enables developers of interactive language education software to use state-of-the-art speech recognition and pronunciation scoring technology. Automatic pronunciation scoring allows the computer to provide feedback on the overall quality of pronunciation and to point to specific production problems. We review our approach to pronunciation scoring, where our aim is to estimate the grade that a human expert would assign to the pronunciation quality of a paragraph or a phrase. Using databases of nonnative speech and corresponding human ratings at the sentence level, we evaluate different machine scores that can be used as predictor variables to estimate pronunciation quality. For more specific feedback on pronunciation, the EduSpeak toolkit supports a phone-level mispronunciation detection functionality that automatically flags specific phone segments that have been mispronounced. Phone-level information makes it possible to provide the student with feedback about specific pronunciation mistakes.Two approaches to mispronunciation detection were evaluated in a phonetically transcribed database of 130,000 phones uttered in continuous speech sentences by 206 nonnative speakers. Results show that classification error of the best system, for the phones that can be reliably transcribed, is only slightly higher than the average pairwise disagreement between the human transcribers.