A critical requirement for studying cell mechanics is three-dimensional assessment of cellular shapes and forces with high spatiotemporal resolution. Traction force microscopy with fluorescence imaging enables the measurement of cellular forces, but it is limited by photobleaching and a slow acquisition speed. Here, we present refractive-index traction force microscopy (RI-TFM), which simultaneously quantifies the volumetric morphology and traction force of cells using a high-speed illumination scheme with 0.5-Hz temporal resolution. Without labelling, our method enables quantitative analyses of dry-mass distributions and shear (in-plane) and normal (out-of-plane) tractions of single cells on the extracellular matrix. When combined with a constrained total variation-based deconvolution algorithm, it provides 0.55-Pa shear and 1.59-Pa normal traction sensitivity for a 1-kPa hydrogel substrate. We demonstrate its utility by assessing the effects of compromised intracellular stress and capturing the rapid dynamics of cellular junction formation in the spatiotemporal changes in non-planar traction components.

Speech is a natural communication method used by humans. Speaker identification (SI) technology based on human speech has been used as an entry point for many human-computer-interaction applications. The performance of SI models can degrade when dealing with expressive speech uttered in emotional situations because emotion databases do not have sufficient data on expressive speech to train SI models for various emotional states. Generally, SI models are trained using relatively more samples of “neutral” speech than samples of other emotion classes. In this study, we propose an emotion-aware SI (em-SI) method that uses an emotion-embedding vector generated from a pre-trained speech emotion recognition (SER) model along with the acoustic features of speech data. We assess the performance of this method using individual English and Korean corpora and confirm that the proposed method provides an improved performance on multilingual corpora. The evaluation results show that the SI accuracy of em-SI on the Korean Emotion Multimodal Database (KEMDy19) improved by 3.2%, and the average speaker verification (SV) performance in terms of the equal error rate (EER) was improved by 1.3% compared to that of the baseline SI model. The visualization of the embedding vector of em-SI shows that em-SI maps speech data to an embedding space where both SI and emotional information are simultaneously represented. Through the experiments conducted in this study, we confirmed that the em-SI model, which learns by integrating emotion and speaker embedding information, improved the performance of SI for expressive speech.