Perceiving a target speech in a multi-speaker environment is common. Consequently, the ability to recognize a target syllable (vowel and consonant) in the presence of another syllable is essential for speech perception. Human ability to recognize individual vowels from the simultaneous presentation has been the subject of many studies in English. Nonetheless, studies to recognize mixed consonants and vowels from simultaneous presentations have received little attention. There are few studies in English, but similar studies in Mandarin could not be found.

This study examines syllable recognition when listening to concurrent pairs in Mandarin. In experiment 1, concurrent Mandarin vowels recognition was studied. The accuracy of recognizing two concurrent tonal vowels increased wit...[ Read more ]

Perceiving a target speech in a multi-speaker environment is common. Consequently, the ability to recognize a target syllable (vowel and consonant) in the presence of another syllable is essential for speech perception. Human ability to recognize individual vowels from the simultaneous presentation has been the subject of many studies in English. Nonetheless, studies to recognize mixed consonants and vowels from simultaneous presentations have received little attention. There are few studies in English, but similar studies in Mandarin could not be found.

This study examines syllable recognition when listening to concurrent pairs in Mandarin. In experiment 1, concurrent Mandarin vowels recognition was studied. The accuracy of recognizing two concurrent tonal vowels increased with the logarithmic spectral contrasts between the two vowels. This finding is new as past studies only showed linear increases. Experiments 2 and 3 extended the range of concurrent syllables by adding an initial consonant to vowels, which formed a “consonant + vowel (CV)”-pattern syllable. Results indicated that increasing both consonant and vowel spectral contrast can improve recognition performance. In addition, the recognition accuracy was further evaluated in terms of consonants, vowels and tones separately. A power function model was fitted to construct the relationship among consonants, vowels, tones and syllables recognition performance. The weighting coefficients of the model revealed that the relative contributions of consonants were more than vowels and tones in concurrent syllable recognition. Moreover, a deep-learning model, trained to separate speech, was programmed to separate concurrent syllables. A comparison of human performance with the deep-learning models indicated that both human and machine performance was significantly affected by spectral differences in concurrent vowels. Interestingly, in concurrent syllables separation when different categories of consonants were involved, similar findings were found for model recognizing syllables differed on consonants. The effect of spectral contrasts was not significant when both vowel differences and consonant differences were presented. Possible insights in model training are discussed.