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--- |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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size_categories: |
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- 10K<n<100K |
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--- |
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[ArXiv Paper Publication Here: "Real-World En Call Center Transcripts Dataset with PII Redaction"](https://arxiv.org/abs/2507.02958) |
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This dataset includes **91,706 high-quality transcriptions** corresponding to approximately **10,500 hours** of **real-world call center conversations** in **English**, collected across various industries and global regions. The dataset features both **inbound and outbound** calls and spans multiple accents, including **Indian**, **American**, and **Filipino** English. All transcripts have been **carefully redacted for PII** and enriched with **word-level timestamps** and **ASR confidence scores**, making it ideal for training robust speech and language models in real-world scenarios. |
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* 🗣️ **Language & Accents**: English (Indian, American, Filipino) |
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* 📞 **Call Types**: Inbound and outbound customer service conversations |
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* 🏢 **Source**: Sourced via partnerships with BPO centers across a range of industries |
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* 🔊 **Audio Length**: 10,500+ hours of corresponding real-world audio (not included in this release) |
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* 📄 **Transcripts**: 91,706 JSON-formatted files with: |
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* Word-level timestamps |
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* ASR confidence scores |
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* Categorized by domain, topic, and accent |
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* Redacted for privacy |
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🔧 **Processing Pipeline**: |
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1. Raw, uncompressed audio was downloaded directly from BPO partners to maintain acoustic integrity. |
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2. Calls were tagged by **domain**, **accent**, and **topic** (inbound vs outbound). |
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3. Transcription was done using **AssemblyAI’s paid ASR model**. |
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4. Transcripts and audios were **redacted for PII** based on the following list: |
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``` |
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account_number, banking_information, blood_type, credit_card_number, credit_card_expiration, |
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credit_card_cvv, date, date_interval, date_of_birth, drivers_license, drug, duration, |
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email_address, event, filename, gender_sexuality, healthcare_number, injury, ip_address, |
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language, location, marital_status, medical_condition, medical_process, money_amount, |
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nationality, number_sequence, occupation, organization, passport_number, password, person_age, |
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person_name, phone_number, physical_attribute, political_affiliation, religion, statistics, |
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time, url, us_social_security_number, username, vehicle_id, zodiac_sign |
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``` |
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5. A manually QA’d subset was used to calculate **word error rate (WER)**, with the overall transcription accuracy estimated at **96.131%**. |
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6. Final output is provided in **JSON format**, with cleaned and standardized fields. |
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📜 **Paper Coming Soon**: A detailed paper describing the full pipeline, challenges, and benchmarks is now published here: https://arxiv.org/abs/2507.02958 |
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📣 **Want Updates?** Drop a comment in the **community section** to be notified when the paper goes live. |
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🔐 **License**: Provided **strictly for research and AI model development**. **Commercial use, resale, or redistribution is prohibited.** |
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🎓 **Brought to you by AIxBlock and 3 independent researchers: Gaurav Chawla, Raghu Banda, Caleb DeLeeuw ** |
