| --- |
| 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`. |
|
|
