BERSt
Full name: Basic Emotion Random phrase Shouts

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

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

Introducing paper: BERSting at the Screams: A Benchmark for Distanced, Emotional and Shouted Speech Recognition