Semi-supervised learning for continuous emotional intensity controllable speech synthesis with disentangled representations

Recent text-to-speech models have reached the level of generating natural speech similar to what humans say. But there still have limitations in terms of expressiveness. The existing emotional speech synthesis models have shown controllability using interpolated features with scaling parameters in emotional latent space. However, the emotional latent space generated from the existing models is difficult to control the continuous emotional intensity because of the entanglement of features like emotions, speakers, etc. In this paper, we propose a novel method to control the continuous intensity of emotions using semi-supervised learning. The model learns emotions of intermediate intensity using pseudo-labels generated from phoneme-level sequences of speech information. An embedding space built from the proposed model satisfies the uniform grid geometry with an emotional basis. The experimental results showed that the proposed method was superior in controllability and naturalness.Comment: Accepted by Interspeech 202

Modeling of a methanol synthesis process to utilize CO2 in the exhaust gas from an engine plant

We investigated the conversion of CO2 in the exhaust gas of an engine plant into methanol. The process consists of CO2 purification by an acid gas removal unit (AGRU), mixed reforming, and methanol synthesis. The AGRU removes a large amount of inert gas, yielding CO2 of 98% purity at a recovery rate of 90% for use as feed to the reformer. The reformer temperature of 900 degrees C led to the almost total consumption of CH4. In the methanol synthesis reaction, the utility temperature had a greater influence on the conversion and methanol production rate than the inlet temperature. The optimal temperature was determined as 180 degrees C. Because the amount of hydrogen in the reformer effluent produced by dry reforming was insufficient, the steam available in the engine plant was used for mixed (dry and steam) reforming. The steam increased the hydrogen and methanol production rate; however, the compression cost was too high, and there exists an optimal amount of steam in the feed. The techno-economic analysis of the optimal conditions showed that utilization of CO2 in the exhaust gas along with freely available steam is economically feasible and reduces CO2 emissions by over 85%.N

Electricity for Fluidics and Bio-Devices

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