| # SoundRisk Dataset | |
| ## Overview | |
| SoundRisk is a multimodal dataset for studying financial communication signals, transcript characteristics, and market-related observations from publicly available earnings calls. | |
| The dataset integrates earnings call transcripts, speaker information, structured annotations, and aligned financial time-series information. It is designed for research on multimodal data analysis, natural language processing, speech analysis, temporal modeling, and empirical financial communication studies. | |
| The dataset is organized into two complementary components: | |
| 1. `soundrisk_nasdaq_earningscall_stockprice_transcripts` | |
| 2. `soundrisk_transcripts_descriptive_stat_analysis_result` | |
| --- | |
| # Dataset Components | |
| ## 1. Earnings Call Transcript and Market Dataset | |
| Directory: | |
| ``` | |
| soundrisk_nasdaq_earningscall_stockprice_transcripts/ | |
| ``` | |
| This dataset contains processed earnings call records, transcript segments, metadata, and aligned market information. | |
| ### Dataset Statistics | |
| - Companies: 283 publicly listed companies | |
| - Earnings call sessions: 1,795 | |
| - Language: English | |
| - Earnings call period: January 2021 – June 2025 | |
| - Stock price history: Daily market observations | |
| - Speaker roles: CEO, CFO, CXO, Analyst, Host, and other participants when available | |
| ### Data Contents | |
| Each company directory may contain: | |
| - Earnings call metadata files | |
| - Transcript files | |
| - Speaker information | |
| - Role annotations | |
| - Stock price records | |
| - Derived alignment information | |
| ### Transcript Information | |
| Transcript records include: | |
| | Field | Description | | |
| |---|---| | |
| | `uid` | Unique earnings call identifier | | |
| | `ticker` | Company ticker | | |
| | `event_time` | Earnings call timestamp | | |
| | `segment_index` | Transcript segment order | | |
| | `speaker` | Speaker identifier from transcript | | |
| | `speaker_role` | Speaker role category | | |
| | `transcripts` | Transcript text segment | | |
| | `token_count` | Segment token count | | |
| ### Annotation Information | |
| The dataset may include derived categorical annotations from transcript and acoustic processing pipelines. | |
| These annotations represent computational outputs and should not be interpreted as verified human psychological states, intentions, personality traits, or emotional ground truth. | |
| ### Market Information | |
| Aligned financial information may include: | |
| - Historical volatility indicators | |
| - Daily returns | |
| - Future return windows | |
| - Realized volatility measurements | |
| Market variables represent historical observations and are not intended for autonomous trading or investment decisions. | |
| --- | |
| # 2. Transcript Descriptive Statistical Analysis Dataset | |
| Directory: | |
| ``` | |
| soundrisk_transcripts_descriptive_stat_analysis_result/ | |
| ``` | |
| This dataset contains statistical summaries and analytical results generated from the processed transcript corpus. | |
| ### Included Information | |
| Examples include: | |
| - Transcript-level descriptive statistics | |
| - Segment-level statistics | |
| - Speaker-role statistics | |
| - Temporal distribution analysis | |
| - Annotation distribution summaries | |
| - Communication pattern measurements | |
| The dataset provides aggregated views of the transcript collection and supports exploratory analysis, benchmarking, and reproducibility of statistical studies. | |
| --- | |
| # Data Processing and Front-End Processing Pipeline | |
| The dataset is constructed through a multimodal processing pipeline that integrates | |
| speech processing, transcript analysis, and financial time-series alignment. | |
| The speech front-end processing component follows a physics-gated streaming | |
| multi-task speech analysis framework, which performs reliable speech representation | |
| extraction, temporal segmentation, and affect-related acoustic feature estimation. | |
| The front-end processing methodology is described in: | |
| ```bibtex | |
| @article{chen2026physics, | |
| title={Physics-Gated Streaming Multi-Task Speech Front-End for Reliability-Weighted Teleconference Affect Aggregation}, | |
| author={Chen, Xiaoliang and Chang, Le and Cai, Yixuan and Huang, Yunhe}, | |
| journal={IEEE Access}, | |
| volume={14}, | |
| pages={70747--70762}, | |
| year={2026}, | |
| doi={10.1109/ACCESS.2026.3687928}, | |
| publisher={IEEE} | |
| } | |
| ``` | |
| --- | |
| # Data Quality Control | |
| Quality checks include: | |
| - Missing file detection | |
| - JSON and transcript format validation | |
| - Duplicate identifier checking | |
| - Annotation consistency checking | |
| - Timestamp validation | |
| - Market alignment verification | |
| Records with incomplete information are retained where possible with explicit missing-value indicators. | |
| --- | |
| # Intended Uses | |
| The dataset is suitable for: | |
| - Multimodal financial communication analysis | |
| - Speech and transcript modeling | |
| - Natural language processing research | |
| - Temporal sequence modeling | |
| - Market communication studies | |
| - Statistical analysis of corporate disclosures | |
| - Benchmark development for multimodal learning systems | |
| --- | |
| # Limitations | |
| Users should consider the following limitations: | |
| - The dataset covers a defined group of companies and time periods. | |
| - Public earnings calls may differ in recording quality and availability. | |
| - Transcript quality depends on the original source material. | |
| - Speaker role assignments may contain errors. | |
| - Derived annotations are computational estimates rather than human-labeled ground truth. | |
| - Market observations do not represent all possible financial responses. | |
| - Results based on this dataset should account for temporal and company-level dependencies. | |
| --- | |
| # Privacy and Ethical Considerations | |
| Earnings calls are public corporate communications. However, transcript records may contain publicly available speaker names and professional roles. | |
| Users should: | |
| - Respect applicable data usage requirements. | |
| - Avoid inferring private attributes from public communication data. | |
| - Avoid using the dataset for employment, insurance, credit, surveillance, or other high-impact decisions. | |
| - Avoid speaker identification, voice cloning, or identity inference applications. | |
| --- | |
| # License and Redistribution | |
| The dataset may contain components originating from different sources, including transcripts, financial records, metadata, and derived analytical outputs. | |
| Users are responsible for: | |
| - Checking applicable source licenses. | |
| - Following copyright and database usage requirements. | |
| - Ensuring appropriate redistribution and usage permissions. | |
| --- | |
| # Reproducibility | |
| The dataset structure is designed to support reproducible analysis workflows. | |
| Recommended practice: | |
| - Preserve dataset versions. | |
| - Record preprocessing configurations. | |
| - Document feature extraction methods. | |
| - Report filtering criteria and missing-data handling procedures. | |
| --- | |
| # Dataset Organization | |
| Recommended directory structure: | |
| ``` | |
| SoundRisk/ | |
| │ | |
| ├── README.md | |
| │ | |
| ├── soundrisk_nasdaq_earningscall_stockprice_transcripts/ | |
| │ | |
| └── soundrisk_transcripts_descriptive_stat_analysis_result/ | |
| ``` | |
| --- | |
| # Contact | |
| For questions regarding dataset structure, corrections, or data usage, please refer to the dataset repository information. |