---
license: apache-2.0
language:
- en
pretty_name: "EarningsCallVoice: Core-100"
size_categories:
- n<1K
task_categories:
- automatic-speech-recognition
- text-to-speech
- audio-classification
tags:
- audio
- datasets
- earnings-calls
- paralinguistics
- voice-cloning
- question-answering
configs:
- config_name: default
  default: true
  data_files:
  - split: core
    path: hf_viewer/core.parquet
---

# EarningsCallVoice: Core-100

EarningsCallVoice is a benchmark family for studying executive vocal delivery
in earnings-call question answering. **Core-100** contains 100 manually
verified units. Each unit provides:

- an authentic reference clip from the executive's prepared remarks;
- the text of an analyst question;
- an authentic answer clip from the same executive in the Q&A;
- exact reference and answer transcripts;
- cryptographic hashes and technical metadata.

The question is text only. The release contains no cloned or otherwise
synthetic speech. No executive names are supplied.

## Why Core-100 exists

The benchmark is designed for controlled research on speaker preservation,
voice cloning, delivery manipulation, and audio information beyond transcripts.
Every admitted unit passed nine human gates covering complete boundaries,
single-speaker purity, text-audio agreement, speaker identity agreement,
question-answer coherence, and overall usability.

The full eligible pool contained 107 units. The public core
was frozen without using delivery labels, model outputs, cloning scores, market
outcomes, or downstream experimental results. It retains all 97
accepted original extractions and adds the three eligible boundary repairs with
the highest frozen reference-answer Resemblyzer similarity. This
tie-break rule was fixed before downstream modeling. The remaining
7 eligible repairs are retained in the provenance ledger as a
reserve set.

## Loading

```python
from datasets import load_dataset

dataset = load_dataset("gmarti/EarningsCallVoice", split="core")
```

The Hub-native view embeds both audio columns for streaming and the Dataset
Viewer. Both are 16 kHz, mono, signed 16-bit PCM WAV. The original WAV files,
CSV, JSONL, and provenance ledger remain available under **Files and
versions**. The immutable, pre-viewer release payload is tagged `v1.0.0`.

As an independent release-level diagnostic, Whisper large-v3-turbo passed all
200 clips under the declared WER and first/last-edge thresholds. Mean normalized
WER was 2.83%. This diagnostic does not replace the human judgments.

## Data statement

- Domain: U.S. public-company earnings calls from 2019 to 2021.
- Language: English, including naturally occurring accents.
- Unit count: 100, one unit per call.
- Authentic reference duration: 12.73 seconds on average.
- Authentic answer duration: 17.56 seconds on average.
- Human validation: one expert reviewer, all nine gates required.
- Speaker identity: verified within each unit, not resolved across calls.
- Selection: source-quality-only and outcome-blind.

This is a small, high-precision benchmark. It is not a representative sample of
all earnings calls, accents, genders, industries, or recording channels. It is
not intended to train a general-purpose speech synthesizer.

## Source and license

Core-100 is derived from FinCall-Surprise, released under Apache-2.0. Its ACL
paper states that the released components include call metadata, transcripts,
speaker audio alignment artifacts, slides, and processing code. See `NOTICE`
for attribution and `LICENSE` for the license text.

## Responsible use

These are voices of real people speaking at public corporate events. See
`RESPONSIBLE_USE.md`. Do not use the clips for impersonation, authentication
bypass, fraud, harassment, or misleading attribution.

## Reproducibility

`provenance/selection_manifest.json` records the frozen input hashes, inclusion
rule, selected repair IDs, reserve IDs, and selection scores. `MANIFEST.sha256`
binds every distributed file.

## Citation

Please cite the forthcoming EarningsCallVoice paper or dataset record and the
upstream FinCall-Surprise paper. A provisional citation is supplied in
`CITATION.cff`.