This study investigated whether adults who stutter and normal adult speakers differ in the production of stop consonants in fluent reading Chinese Putonghua speech.Voice onset time(VOT) was measured and the spectral moments at the stop burst were calculated for the stutterers(both before and after the speech therapy) and also for the nonstutterers. The statistical results showed that there were no significant differences in VOT between the nonstutterers and stutterers either prior to or after therapy,although the mean VOT of the stutterers was slightly greater than that of the nonstutterers.The results also indicated that both the obstruction place and the subsequent syllabic final exhibited an influence to a greater extent on VOT for the stutterers.In the spectral domain,the spectral mean of the stuttering participants before therapy was significantly different from that of the normal participants, whereas the group difference became insignificant after the therapy session.The smaller spectral mean for the stutterers might be interpreted as a more posterior occlusion in the oral cavity when producing alveolars and velars.In addition,productions of the stutterers scattered with a wider range in the space of spectral moments.Furthermore,the smaller main effect of syllabic finals on the mean spectral frequency of the burst suggested that the stutterers exhibited weaker anticipatory coarticulation than the nonstutterers.
A forced alignment based algorithms to detect Chinese repetitive stuttering is studied. According to the features of repetitions in Chinese stuttered speech,improvement solutions are provided based on the previous research findings.First,a multi-span looping forced alignment decoding networks is designed to detect multi-syllable repetitions in Chinese stuttered speech.Second,branch penalty factor is added in the networks to adjust decoding trend using recursive search in order to reduce the error from the complexity of the decoding networks. Finally,we re-judge the detected stutters by calculating confidence to improve the reliability of the detection result.The experimental results show that compared to previous algorithm,the proposed algorithm can improve system performance significantly,about 18%average detection error rate relatively.
A feature extraction technique named perceptual MVDR-based cepstral coefficients (PMCCs) was introduced into speaker recognition.PMCCs are extracted and modeled using Gaussian Mixture Models(GMMs) for speaker recognition.In order to compensate for speaker and channel variability effects,joint factor analysis(JFA) is used.The experiments are carried out on the core conditions of NIST 2008 speaker recognition evaluation data.The experimental results show that the systems based on PMCCs can achieve comparable performance to those based on the conventional MFCCs.Besides,the fusion of the two kinds of systems can make significant performance improvement compared to the MFCCs system alone,reducing equal error rate(EER) by the factor between 7.6%and 30.5%as well as minimum detect cost function (minDCF) by the factor between 3.2%and 21.2%on different test sets.The results indicate that PMCCs can be effectively applied in speaker recognition and they are complementary with MFCCs to some extent.
LIANGChunyan ZHANG Xiang YANG Lin ZHANG Jianping YAN Yonghong
