metadata

license: cc-by-4.0
task_categories:
  - automatic-speech-recognition
  - audio-classification
language:
  - en
tags:
  - shouts
  - emotional_speech
  - distance_speech
  - smartphone_recordings
  - nonsense_phrases
  - non-native_accents
  - regional_accents
pretty_name: B(asic) E(motion) R(andom phrase) S(hou)t(s)
size_categories:
  - 1K<n<10K

BERSt Dataset

We release the BERSt Dataset for various speech recognition tasks including Automatic Speech Recognition (ASR) and Speech Emotion Recogniton (SER)

Overview

9207 single phrase recordings (~12h)
96 professional actors
19 phone positions
7 emotion classes
3 vocal intensity levels
varied regional and non-native English accents
nonsense phrases covering all English Phonemes

Data collection

The BERSt dataset represents data collected in home envrionments using various smartphone microphones (phone model available as metadata) Participants were around the globe and represent varying regional accents in English: UK, Canada, USA (multi-state), Australia, including a subset of the data that is non-native English speakers including: French, Russian, Hindi etc. The data includes 13 non-sense phrases for use cases robust to lingustic context and high surprisal. Partipants were prompted to speak, raise their voice and shout each phrase while moving their phone to various distances and locations in their home, as well as with various obstructions to the microphone, e.g. in a backpack

Baseline results of various state-of-the-art methods for ASR and SER show that this dataset remains a challenging task, and we encourage researchers to use this data to fine-tune and benchmark their models in these difficult condition representing possible real world situations