We present a test corpus of audio recordings and transcriptions of presentations of students' enterprises together with their slides and web-pages. The corpus is intended for evaluation of automatic speech recognition (ASR) systems, especially in conditions where the prior availability of in-domain vocabulary and named entities is benefitable.
The corpus consists of 39 presentations in English, each up to 90 seconds long, and slides and web-pages in Czech, Slovak, English, German, Romanian, Italian or Spanish.
The speakers are high school students from European countries with English as their second language.
We benchmark three baseline ASR systems on the corpus and show their imperfection.
Eyetracked Multi-Modal Translation (EMMT) is a simultaneous eye-tracking, 4-electrode EEG and audio corpus for multi-modal reading and translation scenarios. It contains monocular eye movement recordings, audio data and 4-electrode wearable electroencephalogram (EEG) data of 43 participants while engaged in sight translation supported by an image.
The details about the experiment and the dataset can be found in the README file.
This dataset can serve as a training and evaluation corpus for the task of training keyword detection with speaker direction estimation (keyword direction of arrival - KWDOA).
It was created by processing the existing Speech Commands dataset [1] with the PyroomAcoustics library so that the resulting speech recordings simulate the usage of a circular microphone array with 4 microphones having a distance of 57 mm between adjacent microphones. Such design of a simulated microphone array was chosen in order to match the existing physical microphone array from the Seeeduino series.
[1] Warden, Pete. “Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition.” ArXiv.org, 2018, arxiv.org/abs/1804.03209