694 research outputs found
Unifying Amplitude and Phase Analysis: A Compositional Data Approach to Functional Multivariate Mixed-Effects Modeling of Mandarin Chinese
Mandarin Chinese is characterized by being a tonal language; the pitch (or
) of its utterances carries considerable linguistic information. However,
speech samples from different individuals are subject to changes in amplitude
and phase which must be accounted for in any analysis which attempts to provide
a linguistically meaningful description of the language. A joint model for
amplitude, phase and duration is presented which combines elements from
Functional Data Analysis, Compositional Data Analysis and Linear Mixed Effects
Models. By decomposing functions via a functional principal component analysis,
and connecting registration functions to compositional data analysis, a joint
multivariate mixed effect model can be formulated which gives insights into the
relationship between the different modes of variation as well as their
dependence on linguistic and non-linguistic covariates. The model is applied to
the COSPRO-1 data set, a comprehensive database of spoken Taiwanese Mandarin,
containing approximately 50 thousand phonetically diverse sample contours
(syllables), and reveals that phonetic information is jointly carried by both
amplitude and phase variation.Comment: 49 pages, 13 figures, small changes to discussio
Emotion Analysis of Telephone Complaints from Customer Based on Affective Computing
Customer complaint has been the important feedback for modern enterprises to improve their product and service quality as well as the customer’s loyalty. As one of the commonly used manners in customer complaint, telephone communication carries rich emotional information of speeches, which provides valuable resources for perceiving the customer’s satisfaction and studying the complaint handling skills. This paper studies the characteristics of telephone complaint speeches and proposes an analysis method based on affective computing technology, which can recognize the dynamic changes of customer emotions from the conversations between the service staff and the customer. The recognition process includes speaker recognition, emotional feature parameter extraction, and dynamic emotion recognition. Experimental results show that this method is effective and can reach high recognition rates of happy and angry states. It has been successfully applied to the operation quality and service administration in telecom and Internet service company
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