SpeakingFaces: A Large-Scale Multimodal Dataset of Voice Commands with Visual and Thermal Video Streams
This dataset provides a valuable resource for researchers working on multimodal machine learning tasks, particularly in human-computer interaction and biometrics, by offering synchronized visual, thermal, and audio data.
The authors introduce SpeakingFaces, a large-scale multimodal dataset comprising aligned thermal, visual, and audio streams from 142 subjects speaking 100 imperative phrases, totaling over 13,000 synchronized instances. They demonstrate baseline applications for gender classification and thermal-to-visual facial image translation.
We present SpeakingFaces as a publicly-available large-scale multimodal dataset developed to support machine learning research in contexts that utilize a combination of thermal, visual, and audio data streams; examples include human-computer interaction, biometric authentication, recognition systems, domain transfer, and speech recognition. SpeakingFaces is comprised of aligned high-resolution thermal and visual spectra image streams of fully-framed faces synchronized with audio recordings of each subject speaking approximately 100 imperative phrases. Data were collected from 142 subjects, yielding over 13,000 instances of synchronized data (~3.8 TB). For technical validation, we demonstrate two baseline examples. The first baseline shows classification by gender, utilizing different combinations of the three data streams in both clean and noisy environments. The second example consists of thermal-to-visual facial image translation, as an instance of domain transfer.