Mindy Support Powers Scalable, Accurate AI Transcription in 40+ Languages

LLM Solutions, Audio Transcription

The client, a global media and technology company, sought a trusted partner to support a multilingual audio data annotation initiative. The core objective of the project was to deliver high-quality, human-generated transcriptions and detailed linguistic annotations of existing audio content, such as podcasts, across more than 40 global languages. The project began with English, French, Italian, Dutch, German, Spanish, and Portuguese, and included the global recruitment of professional annotators with native language expertise.

With an ever-growing repository of spoken content in diverse languages, the client faced challenges in structuring this data for further AI training and content enhancement. They required a scalable, high-accuracy transcription and annotation solution that would not only capture words but also include speaker distinctions, non-verbal cues, emotional tone, and precise timestamps – all while ensuring linguistic accuracy and cultural relevance.

Mindy Support was selected based on its proven track record in multilingual data annotation, large-scale team management, and fast deployment capabilities. With extensive experience supporting Large Language Model (LLM) development, handling complex linguistic projects, and recruiting annotators with native-level language expertise, Mindy Support was well-positioned to meet the client’s stringent KPIs for accuracy, consistency, and scalability across a broad linguistic landscape.

1/Initial Transcription: Audio content was transcribed using a proprietary transcription algorithm and platform, fully integrated with QA tools to support automated baseline accuracy.

2/Transcription Verification: Human annotators reviewed the initial transcription output, verifying and correcting errors to meet linguistic and formatting standards.

3/Layered Annotation: Transcripts were enriched with advanced linguistic and contextual features, including:

• Speaker segmentation and identification
• Timestamp alignment at both segment and utterance levels
• Emotional tagging (e.g., anger, joy, sarcasm)
• Non-speech event identification (e.g., chuckles, coughing, background noises)
• Accent and dialect classification
• Full punctuation and casing, tailored to each language’s grammatical structure

4/Metadata Generation: Custom scripts extracted and compiled speaker labels, emotional markers, and other annotated metadata into standardized formats for client delivery.

5/Iterative Quality Review: A dedicated QA team conducted multi-layered reviews using both automated error-detection tools and manual verification to ensure the highest level of annotation accuracy in each batch.

Project Infrastructure

Mindy Support leveraged a secure cloud-based annotation platform that ensured:

• Role-based access control for data protection
• Real-time progress tracking dashboards
• Custom API integrations for delivering structured outputs
• Encrypted file exchange and storage in compliance with GDPR

Regular syncs with the client’s technical team ensured alignment on formatting standards, file structures, and evolving guidelines.

This comprehensive delivery model enabled Mindy Support to maintain both scale and precision, seamlessly delivering large data volumes with consistently high quality.

1/ Accuracy Excellence:

• Word Error Rate (WER) consistently below 1%
• Annotation Accuracy exceeding 95%
• Transcription Accuracy above 98%

2/ Operational Efficiency:

• Batch Acceptance Rate over 99%
• Feedback resolution within 48 hours
• Full adherence to all formatting and annotation guidelines

3/ Scalability Delivered:

• 100% of planned languages covered on schedule
• Successfully scaled from pilot languages to over 40 with sustained quality