We present a pipeline for deep neural network assisted modeling and analysis of the behavior of an acoustic tube. The vocal tract is represented as a series of cylindrical tube segments, each characterized by fixed length and variable cross-sectional area. A large synthetic dataset of such tube configurations is generated, and a circuit theory–based algorithm predicts corresponding formant frequencies. To explore mapping between vocal tract shapes and formant values, the pipeline integrates both linear regression and nonlinear machine learning models - including multilayer perceptrons. Model interpretability is measured using Shapley Additive Explanations (SHAP), which quantifies the contribution of each segment to predicted formant frequencies. The proposed framework enables detailed exploration of the articulatory-acoustic relationships inherent to an acoustic tube and vocal tract simulacrum. We present and describe the pipeline in the context of modeling effects of perturbations on the first three formants for a 16-cm tube, divided into 1 cm segments. Our pipeline can be applied to any method that models predictions of behavior of an acoustic tube, where the tube is conceived as a series of segmented units.

Under the umbrella of anonymous social networks, many women have suffered from abuse, discrimination, and other sexist expressions online. However, exsiting methods based on keyword filtering and matching performed poorly on online sexism detection, which lacked the capability to identify implicit stereotypes and discrimination. Therefore, this paper proposes a System of Ensembling Fine-tuning Models (SEFM) at SemEval-2023 Task 10: Explainable Detection of Online Sexism. We firstly use four task-adaptive pre-trained language models to flag all texts. Secondly, we alleviate the data imbalance from two perspectives: over-sampling the labelled data and adjusting the loss function. Thirdly, we add indicators and feedback modules to enhance the overall performance. Our system attained macro F1 scores of 0.8538, 0.6619, and 0.4641 for Subtask A, B, and C, respectively. Our system exhibited strong performance across multiple tasks, with particularly noteworthy performance in Subtask B. Comparison experiments and ablation studies demonstrate the effectiveness of our system.