The Earnings 22 dataset ( also referred to as earnings22 ) is a 119-hour corpus of English-language earnings calls collected from global companies. The primary purpose is to serve as a benchmark for industrial and academic automatic speech recognition (ASR) models on real-world accented speech.
This work has been submitted for publication at Interspeech 2022.
In the following section, we provide an overview of the file formats we provide with this dataset.
NLP files are .csv inspired, pipe-separated text files that contain token and metadata information of a transcript. Each line of a file represents a single transcript token and the metadata associated with it.
| Column Title | Description |
|---|---|
Column 1: token |
A single token in the transcript. These are typically single words or multiple words with hyphens in between. |
Column 2: speaker |
A unique integer that associates this token to a specific speaker in an audio |
Column 3: ts |
A float representing the start time of the token, in seconds |
Column 4: endTs |
A float representing the end time of the token, in seconds |
Column 5: punctuation |
A punctuation character that is included at the end of a token that is used when reconstructing the transcript. Example punctuation: ",", ";", ".", "!". |
Column 6: case |
A two letter code to denominate the which of four possible casings for this token:
|
Column 7: tags |
Displays one of the several entity tags that are listed in wer_tags in long form - such that the displayed entity here is in the form ID:ENTITY_CLASS. |
Column 8: wer_tags |
A list of entity tags that are associated with this token. In this field, only entity IDs should be present. The specific ENTITY_CLASS for each ID can be extracted from an accompanying wer_tags sidecar json. |
Note that each entity ID is unique to that specific entity. Entities can be comprised of single and multiple tokens. Within a file there can be several entities of the same ENTITY_CLASS but only one entity can be labeled with any given ID.
example.nlp
token|speaker|ts|endTs|punctuation|case|tags|wer_tags
Good|0||||UC|[]|[]
morning|0||||LC|['5:TIME']|['5']
and|0||||LC|[]|[]
welcome|0||||LC|[]|[]
to|0||||LC|[]|[]
the|0||||LC|['6:DATE']|['6']
first|0||||LC|['6:DATE']|['6']
quarter|0||||LC|['6:DATE']|['6']
2020|0||||CA|['0:YEAR']|['0', '1', '6']
NexGEn|0||||MC|['7:ORG']|['7']
Tables found in the paper along with all entity class WER can be found within the transcripts directory.
All of our analysis on this dataset is done through the use of our newly released fstalign tool. We strongly recommend the use of this tool to quickly get started using the Earnings-22 dataset.
This dataset has been submitted to Interspeech 2022. The paper describing our methods and results can be found on arXiv at https://arxiv.org/abs/2203.15591.
@misc{https://doi.org/10.48550/arxiv.2203.15591,
doi = {10.48550/ARXIV.2203.15591},
url = {https://arxiv.org/abs/2203.15591},
author = {Del Rio, Miguel and Ha, Peter and McNamara, Quinten and Miller, Corey and Chandra, Shipra},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Earnings-22: A Practical Benchmark for Accents in the Wild},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution Share Alike 4.0 International}
}