The Clarkson University Affective Data Set (CUADS) is a multi-modal affective dataset designed to assist in machine learning model development for automated emotion recognition. CUADS provides electrocardiogram, photoplethysmogram, and galvanic skin response data from 38 participants, captured under controlled conditions using Shimmer3 ECG and GSR sensors. ECG, GSR and PPG signals were recorded while each participant viewed and rated 20 affective movie clips. CUADS also provides big five personality traits for each participant. All data have been anonymized to protect participant privacy.

Introduced here is the Extended-Length Audio Dataset for Synthetic Voice Detection and Speaker Recognition (ELAD-SVDSR), a resource designed to advance research in synthetic voice (DeepFake) detection and automatic speaker recognition (ASR). It features around 45-minute audio recordings from 36 participants, each of whom read aloud different newspaper articles during controlled sessions, captured with five different high-quality microphones. Synthetic voices generated from 20 subjects of this dataset using open-source and commercial software are also included. Supporting text-dependent  analysis, the dataset may enable diverse ASR modeling. This extended-duration audio may allow for the detection of nuanced artifacts and the generation of higher-quality synthetic samples, including those like Tortoise TTS and ElevenLabs, which already excel in shorter segments. Comprehensive metadata on speaker demographics and recording conditions are expected to provide deeper insights into voice characteristics and model efficacy.  Publicly accessible, while all personal data has been anonymized to ensure privacy, ELAD-SVDSR is expected to drive significant advancements in biometric security, audio forensics, and voice authentication systems.

Voice Pre-processing and Quality Assessment Dataset (VPQAD), a scalable resource has been developed to validate various pre-processing techniques and improve voice signal quality in noisy environments. The dataset comprises voice recordings from 50 participants aged 18 to 40, captured in controlled real-life conditions using Audio Technica AT2020 and SHURE SM58 microphones. These high-quality recordings, made under diverse noise levels and settings, could be used for testing and developing voice enhancement algorithms. The dataset includes detailed metadata on the environment and participant demographics for analyzing and improving speech clarity and intelligibility, particularly in challenging conditions. To protect privacy, all data have been anonymized. VPQAD has been made public to promote collaborative research and advance research in biometrics, telecommunications, assistive technologies, and other applications requiring clear voice communication.