标题： 基于不确定性感知的Whisper嵌入和sLSTM的语音可懂度评估 显示英文标题

标题： Speech Intelligibility Assessment with Uncertainty-Aware Whisper Embeddings and sLSTM

摘要： 非侵入式语音可懂度预测由于说话人差异、噪声条件和主观感知的多样性而仍然具有挑战性。 我们提出了一种考虑不确定性的方法，该方法结合Whisper嵌入和统计特征，特别是跨嵌入维度计算的均值、标准差和熵。 通过特征维度上的softmax计算得到的熵作为不确定性的代理，补充了由均值和标准差捕获的全局信息。 为了建模语音的序列结构，我们采用了一个标量长短期记忆（sLSTM）网络，该网络能够高效地捕捉长距离依赖关系。 在此基础上，我们提出了iMTI-Net，一种改进的多目标可懂度预测网络，在多任务学习框架中集成了卷积神经网络（CNN）和sLSTM组件。 它联合预测人类可懂度分数以及来自Google ASR和Whisper的基于机器的词错误率（WER）。 实验结果表明，iMTI-Net在多个评估指标上优于原始的MTI-Net，证明了引入考虑不确定性的特征以及CNN-sLSTM架构的有效性。 显示英文摘要

摘要： Non-intrusive speech intelligibility prediction remains challenging due to variability in speakers, noise conditions, and subjective perception. We propose an uncertainty-aware approach that leverages Whisper embeddings in combination with statistical features, specifically the mean, standard deviation, and entropy computed across the embedding dimensions. The entropy, computed via a softmax over the feature dimension, serves as a proxy for uncertainty, complementing global information captured by the mean and standard deviation. To model the sequential structure of speech, we adopt a scalar long short-term memory (sLSTM) network, which efficiently captures long-range dependencies. Building on this foundation, we propose iMTI-Net, an improved multi-target intelligibility prediction network that integrates convolutional neural network (CNN) and sLSTM components within a multitask learning framework. It jointly predicts human intelligibility scores and machine-based word error rates (WER) from Google ASR and Whisper. Experimental results show that iMTI-Net outperforms the original MTI-Net across multiple evaluation metrics, demonstrating the effectiveness of incorporating uncertainty-aware features and the CNN-sLSTM architecture.