Recent advancements in audio diffusion models have significantly improved text-to-audio editing via inversion techniques. However, these models typically rely on dense, fixed-step sampling trajectories to maintain structural integrity during inversion and generation, leading to prohibitive computational costs. We propose AdaTE, a model-agnostic Adaptive Trajectory Extrapolation framework that accelerates the inversion-based editing process by dynamically evaluating only the most critical generative phases. Specifically, we introduce a hierarchical probing mechanism that monitors curvature acceleration and information gain to detect pivotal transitions within the latent flow. This allows the model to selectively skip redundant segments via linear extrapolation while preserving dense neural evaluations for complex semantic changes. Extensive experiments across AudioLDM2, Auffusion, and Tango2 demonstrate that AdaTE achieves up to a 3.9× speedup with negligible loss in fidelity. AdaTE significantly shifts the Pareto frontier, providing an efficient solution for high-fidelity audio synthesis and editing.

Dataset Pruning (DP) aims to construct a coreset that achieves performance comparable to the original, full dataset. However, few studies have explored DP in the context of Speech Classification (SC) tasks. Unlike image or text classification, SC is particularly challenging due to the difficulty in capturing the acoustic, semantic, and contextual representations. In this study, we propose a novel dataset pruning method for speech datasets, termed Meltrim, which uses a two-step coarse-to-fine framework designed to address these challenges. Specifically, in Step 1, Meltrim coarsely filters utterance-level redundant samples using DBSCAN clustering on Mel-Frequency Cepstral Coefficients (MFCC) features, which are first flattened and then reduced in dimensionality using UMAP. In Step 2, we perform frame-level redundancy pruning for each utterance via utility pruning, which aims to eliminate irrelevant frames within each utterance. To the best of our knowledge, this is the first dataset pruning approach designed for Speech Classification tasks, demonstrating outstanding performance compared to classical general DP methods. Notably, for the Speech Emotion Recognition, our method achieves up to a 49.5% improvement in WA (Weighted Accuracy) on the MEAD dataset. For the Speaker Identification tasks, it results in a 41.9% reduction in EER (Equal Error Rate) on the VoxCeleb1 dataset.