Omkar2118/datasets huggingface dataset:
Colored-MNIST(with spurious correlation)

This is a dataset with spurious correlations which can be used to evaluate machine learning methods for out-of-distribution generalization, causal inference, and related field.

Introducing paper: Invariant Risk Minimization
JaQuAD

**JaQuAD** (Japanese Question Answering Dataset) is a question answering dataset in Japanese that consists of 39,696 extractive question-answer pairs on Japanese Wikipedia articles.

Introducing paper: JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension
Tool clustering dataset

# Tool Database for image-set clustering


This database was generated to evaluate a robotic application dealing with image-set clustering. The goal is to sort and store tools on an table in an unsupervised way, from pixel inputs. Pictures contains objects that can be found in a shop-floor. Each picture contains only one object. There are five different conditions, for each condition, lighting conditions and background are changed. For each condition, four picture of each object are taken under different orientations.

Introducing paper: CNN features are also great at unsupervised classification
EmoFilm
Full name: Emotional speech from Films

EmoFilm is a multilingual emotional speech corpus comprising 1115 audio instances produced in English, Italian, and Spanish languages. The audio clips (with a mean length of 3.5 sec. and std 1.2 sec.) were extracted in wave format (uncompressed, mono, 48 kHz sample rate and 16-bit) from 43 films (original in English and their over-dubbed Italian and Spanish versions). Genres including comedy, drama, horror, and thriller were considered; anger, contempt, happiness, fear, and sadness emotional states were taken into account. EmoFilm has been presented at Interspeech 2018:
Emilia Parada-Cabaleiro, Giovanni Costantini, Anton Batliner, Alice Baird, and Björn Schuller (2018), Categorical vs Dimensional Perception of Italian Emotional Speech, in Proc. of Interspeech, Hyderabad, India, pp. 3638-3642.
SES
Full name: Spanish Emotional Speech

Currently, an essential point in speech synthesis is the addressing of the variability of human speech. One of the main sources of this diversity is the emotional state of the speaker. Most of the recent work in this area has been focused on the prosodic aspects of speech and on rule-based formant synthesis experiments. Even when adopting an improved voice source, we cannot achieve a smiling happy voice or the menacing quality of cold anger. For this reason, we have performed two experiments aimed at developing a concatenative emotional synthesiser, a synthesiser that can copy the quality of an emotional voice without an explicit mathematical model.
AESI
Full name: Athens Emotional States Inventory

The development of ecologically valid procedures for collecting reliable and unbiased emotional data towards computer interfaces with social and affective intelligence targeting patients with mental disorders. Following its development, presented with, the Athens Emotional States Inventory (AESI) proposes the design, recording and validation of an audiovisual database for five emotional states: anger, fear, joy, sadness and neutral. The items of the AESI consist of sentences each having content indicative of the corresponding emotion. Emotional content was assessed through a survey of 40 young participants with a questionnaire following the Latin square design. The emotional sentences that were correctly identified by 85% of the participants were recorded in a soundproof room with microphones and cameras. A preliminary validation of AESI is performed through automatic emotion recognition experiments from speech. The resulting database contains 696 recorded utterances in Greek language by 20 native speakers and has a total duration of approximately 28 min. Speech classification results yield accuracy up to 75.15% for automatically recognizing the emotions in AESI. These results indicate the usefulness of our approach for collecting emotional data with reliable content, balanced across classes and with reduced environmental variability.