---
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

* 4526 single phrase recordings (~3.75h)
* 98 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

Affect annotations are those provided to the actors, they have not been validated through perception
The speech annotations, however, has been checked and adjusted to mistakes in the speech.

## Data splits and organisation

For each phone position and phrase, the actors provided a single recording for the three vocal intensity levels, these raw audio files are available

Meta-data in csv format corresponds to the files split per utterance with noise and silence before and after speech removed, found inside `clean_clips` for each data splits

We provide a test, train and validation split

There is no speaker cross-over between splits, the train and validation sets each contain 10 speakers not seen in the training set

## Baseline Results
TBD

## Metadata Details

* actor count
  * 98
* Gender counts
  * Woman: 61
  * Man: 34
  * Non-Binary: 1
  * Prefer not to disclose 2
* Current daily language counts
  * English: 95
  * Norwegian: 1
  * Russian: 1
  * French: 1
* First language counts
  * English: 75
  * Non English: 23
    * Spanish: 6
    * French: 3
    * Portuguese: 3
    * Chinese: 2
    * Norwegian: 1
    * Mandarin: 1
    * Tagalog: 1
    * Italian: 1
    * Hungarian: 1
    * Russian: 1
    * Hindi: 1
    * Swahili: 1
    * Croatian: 1
Pre-split Data counts
* Emotion counts
  * fear: 236
  * neutral: 234
  * disgust: 232
  * joy: 224
  * anger: 223
  * surprise: 210
  * sadness: 201
* Distance counts:
  * Near body: 627
  * 1-2m away: 324
  * Other side of room: 316
  * Outside of room: 293