Automated Multi-Speaker Call Transcription and Entity Recognition System

The client faces difficulty maintaining focus during long conference calls due to manual note-taking requirements. Existing solutions lack accurate speaker differentiation, structured transcription, and real-time recognition of critical entities, leading to information loss and reduced productivity.

A mid-to-large size enterprise specializing in client support and communication management, seeking to enhance operational efficiency through automated call processing and documentation.

• Develop an automated call transcription system capable of accurately converting speech to text with timestamp and speaker labels.
• Implement speaker diarization to distinguish and label up to 20 participants within a call.
• Enable detection and extraction of named entities such as persons, organizations, locations, and dates for improved data retrieval.
• Provide a user-friendly export of transcripts with integrated timestamps, speaker identification, and entity details.
• Improve call efficiency and decision-making by providing comprehensive, structured, and accessible meeting records.

• Voice activity detection to identify segments containing speech.
• Speaker recognition and diarization for up to 20 participants with label assignment.
• Speech-to-text transcription incorporating punctuation and capitalization.
• Named entity recognition to identify and list crucial entities such as persons, places, dates, and organizations.
• Timestamping of each spoken segment and associated speaker label.
• Export functionality for transcription data with integrated timestamps, speaker labels, and entities in a structured format.

• Audio input sources from call recording systems or telephony platforms
• Data storage solutions for saving and retrieving transcripts
• Client internal dashboards or analytics platforms for review and analysis

• System must process calls in real-time or near real-time with minimal latency
• Support recording durations typical of enterprise calls (up to 2 hours or more)
• Handle simultaneous processing of multiple call recordings
• Ensure data privacy and compliance with relevant regulations (e.g., GDPR)
• Achieve >90% accuracy in transcription and entity recognition after fine-tuning

Implementing this automated transcription and entity recognition system is expected to significantly increase meeting productivity, reduce administrative workload, and improve accuracy of documented call details. This will lead to faster decision-making and better information retention, with an estimated enhancement in communication efficiency by over 30% and improved data accessibility for business operations.