Tone Enhancing Model for Disyllable Words in Chinese Mandarin Speech

Tone recognition is the core function in Chinese speech perception. The tone perception ability of people with sensorineural hearing loss (SNHL) is often weaker than normal people. Automatically tone enhancement would be useful in helping them understand Chinese speech better. In this paper, we focus on the tone enhancing model for Chinese disyllable words. We first analyze the acoustic features related to tone perception. By agglomerative hierarchical clustering method, the first and second syllables of disyllable words are clustered into 6 clusters respectively. Discriminative features of these clusters are experimentally determined from a set of possible features related to tone perception, such as the pitch value, pitch range and position of minimum pitch, etc. We further propose a practicable tone enhancing model with these discriminative features: 1) an input pitch contour is classified by calculating the distance between it and the centroid of each cluster, and 2) selecting the smallest distance, then the unclassified pitch contour belongs to this cluster, 3) the pitch contour is modified for tone enhancement with model parameters corresponding to this cluster using TD-PSOLA. Both statistical and subjective experiments show that higher hit rate of tone recognition can be obtained after tone enhancement with the proposed model. Especially, the proposed enhancing model can also avoid traditional tone recognition, which is more convictive and less laborious.