Effect of Music Training on Categorical Perception of Speech and Music

Background and Objectives: The aim of this study is to evaluate the effect of music training on the characteristics of auditory perception of speech and music. The perception of speech and music stimuli was assessed across their respective stimulus continuum and the resultant plots were compared between musicians and non-musicians. Subjects and Methods: Thirty musicians with formal music training and twenty-seven non-musicians participated in the study (age: 20 to 30 years). They were assessed for identification of consonant-vowel syllables (/da/ to /ga/), vowels (/u/ to /a/), vocal music note (/ri/ to /ga/), and instrumental music note (/ri/ to /ga/) across their respective stimulus continuum. The continua contained 15 tokens with equal step size between any adjacent tokens. The resultant identification scores were plotted against each token and were analyzed for presence of categorical boundary. If the categorical boundary was found, the plots were analyzed by six parameters of categorical perception; for the point of 50% crossover, lower edge of categorical boundary, upper edge of categorical boundary, phoneme boundary width, slope, and intercepts. Results: Overall, the results showed that both speech and music are perceived differently in musicians and non-musicians. In musicians, both speech and music are categorically perceived, while in non-musicians, only speech is perceived categorically. Conclusions: The findings of the present study indicate that music is perceived categorically by musicians, even if the stimulus is devoid of vocal tract features. The findings support that the categorical perception is strongly influenced by training and results are discussed in light of notions of motor theory of speech perception.

A Bayesian Approach to Phoneme Detection for Uyghur

In this paper, we presented a semantic speech segmentation approach, in particular phoneme segmentation. In order to get phoneme level information, a novel voiced speech, unvoiced speech and silence (VUS) classification is proposed. Five parameters that can be extracted by short-time analysis methods are used to discriminate the phoneme boundary. Experiments on Uyghur broadcasting news indicate that the performance of proposed algorithm is satisfying.