Recent advancements in diffusion models and large language models (LLMs) have significantly propelled the field of generation tasks. Text-to-Audio (TTA), a burgeoning generation application designed to generate audio from natural language prompts, is attracting increasing attention. However, existing TTA studies often struggle with generation quality and text-audio alignment, especially for complex textual inputs. Drawing inspiration from state-of-the-art Text-to-Image (T2I) diffusion models, we introduce Auffusion, a TTA system adapting T2I model frameworks to TTA task, by effectively leveraging their inherent generative strengths and precise cross-modal alignment. Our objective and subjective evaluations demonstrate that Auffusion surpasses previous TTA approaches using limited data and computational resources. Furthermore, the text encoder serves as a critical bridge between text and audio, since it acts as an instruction for the diffusion model to generate coherent content. Previous studies in T2I recognize the significant impact of encoder choice on cross-modal alignment, like fine-grained details and object bindings, while similar evaluation is lacking in prior TTA works. Through comprehensive ablation studies and innovative cross-attention map visualizations, we provide insightful assessments, being the first to reveal the internal mechanisms in the TTA field and intuitively explain how different text encoders influence the diffusion process. Our findings reveal Auffusion's superior capability in generating audios that accurately match textual descriptions, which is further demonstrated in several related tasks, such as audio style transfer, inpainting, and other manipulations.

Physiological reports have confirmed that there are differences in speech signals between depressed and healthy individuals. Therefore, as an application in the field of affective computing, automatic depression level prediction through speech signals has received the attention of researchers, which often estimate the depression severity of individuals by the Fourier or Mel spectrograms of speech signals. However, some studies on speech emotion recognition suggest that directly modeling the raw speech signal is more helpful for extracting emotion-related information. Inspired by this fact, we develop a WavDepressionNet to model raw speech signals for the improvement of prediction accuracy. In our method, a representation block is proposed to find a set of basis vectors to construct the optimal transformation space and generate the transformation result (named Depression Feature Map, DFM) of speech signal for facilitating the perception of depression cues. We further propose an assessment block, which cannot only use the designed spatiotemporal self-calibration mechanism to calibrate the DFM and highlight the useful elements, but also aggregates the calibrated DFM across various temporal ranges with the dilated convolution. Experimental results on the AVEC 2013 and AVEC 2014 depression databases demonstrate the effectiveness of our approach over previous works.

Physiological studies have shown that healthy and depressed individuals present different facial changes. Thus, many researchers have attempted to use Convolutional Neural Networks (CNNs) to extract high-level facial dynamic representations for predicting depression severity. However, the max-pooling (or average-pooling) layers in the CNN lead to the loss of subtle depression cues. Without pooling layers, the CNN cannot extract multi-scale information and has difficulties for tensor vectorization. To this end, we propose a Selective Element and Two Orders Vectorization (SE-TOV) network. For the SE-TOV network, an SE block is constructed to adaptively select the effective elements from the tensors obtained by receptive fields of different sizes. Moreover, we propose a TOV block for vectorizing a high-dimensional tensor. On the one hand, TOV block inputs a tensor into the Global Average Pooling layer to obtain the first-order vectorization result. On the other hand, it takes principal components of the correlation matrix of channels in a tensor as the second-order vectorization result. Experimental results on AVEC 2013 (RMSE=7.42, MAE=6.09) and AVEC 2014 (RMSE=7.39, MAE=5.87) depression databases illustrate the superiority of our approach over previous works.