Yanqin Li’s research while affiliated with Northwest Normal University and other places

Emotional speech synthesis is expected to make the synthesized speech more expressive. In order to synthesize more natural emotional speech signals, this paper presents a method to realize HMM based emotional speech synthesis using a Mandarin speech synthesis framework. A Mandarin context-dependent label format is adopted to label emotional sentences. A Mandarin question set is also extended for emotional sentences by adding language-specific questions. A Mandarin speech synthesis framework is utilized to train an average voice model from a large Mandarin multi speaker-based corpus and a small emotional one-speaker-based corpus using the Speaker Adaptive Training. Then the speaker adaptation transformation is applied to the average voice model to obtain a speaker-adapted emotional model. Experimental results show that in case of the same emotional corpus, this method proposed outperforms the method using the speaker dependent emotional model when the number of training Mandarin utterances is increased.

In recent years, speech-based depression detection has gained some initial interest in China, but the lack of publicly available Chinese depression corpus leads to the difficulty of comparing its features and results. According to psychological theory, the pervasive hopelessness and high risk of suicide in depressive patients suggested that the self might be abnormal among them. It highlights many of the advantages of self-related processing can contribute to corpus design. In this paper, a novel design idea of Chinese depression spoken speech corpus based on psychological self-related processing is outlined in this paper. To do so, we therefore introduce some novel methods such as different psychological self and psychological experimental paradigms. The data and collection method have been used in our study.