Collecting 780+ Hours of Bilingual Conversational Data Across 60+ Language Pairs for a Global Speech-AI Leader

audio data collection, linguistic annotation (transcription, timestamps, speaker turns, language tags), Technical audio processing and delivery, quality assurance

The project required both common global languages and complex regional pairs. Mindy Support delivered bilingual data in combinations including:

English ↔ Spanish, English ↔ French, English ↔ Arabic, English ↔ German Hindi ↔
Arabic ↔ German, Arabic ↔ French, German ↔ Russian, Tamil ↔ Malayalam, Telugu ↔ Kannada, Ukrainian ↔ Polish, Tagalog ↔ Malay, Vietnamese ↔ Korean

This multilingual diversity helped the client strengthen their speech recognition and translation accuracy across multiple linguistic families.

Although the client’s speech translation engine was already well-developed, it struggled with real human conversation – fast, emotional, accented, and often noisy. Their existing datasets were clean and scripted, which limited the model’s ability to generalize in real-world scenarios. To solve this, Mindy Support designed and managed a global bilingual data-collection operation producing:

• Over 780 hours of spontaneous conversations
• 60+ language pairs
• 8 conversation scenarios (travel, negotiation, emergencies, personal advice, etc.)
• Real-world acoustic conditions – markets, cafés, homes, transportation hubs

Our teams coordinated speaker sourcing, scenario guidance, environmental requirements, transcription workflows, metadata structuring, and multilayer QA to ensure every recording met strict technical and linguistic standards.

The client needed to overcome several core issues:

• Lack of real conversational speech data to train translation models for natural interactions
• Limited accent and dialect coverage, especially for Indic and regional languages
• Poor performance in noisy environments, where customers frequently use speech features
• High technical requirements for audio formatting, structure, and metadata
• Scalability challenges – capturing 60+ language pairs internally was not feasible

Mindy Support stood out due to the ability to deliver a complete multilingual data pipeline:

• Global network of native speakers across 25+ countries
• Deep expertise in speech AI training datasets, including ASR, TTS, and S2S translation
• Strict alignment to audio specifications (WAV, LINEAR16, sample rates, mono)
• Robust QA system ensuring accuracy of transcripts, segmentation, and metadata
• Large-scale operational capability across time zones and languages
• Collaborative engineering-to-linguistics workflow with the client’s ML team

Our combination of linguistic insight and technical precision made us the ideal partner for this high-complexity project.

1/ Natural Bilingual Conversations Across 60+ LPs

Two native speakers recorded spontaneous, unscripted conversations across eight scenarios. The dialogues captured real emotional tone, interruptions, hesitations, different speaking speeds, and authentic contextual flow.

Recordings took place in acoustically varied locations such as cafés, train stations, home environments, markets, and outdoor spaces – ensuring realistic noise patterns essential for speech model robustness.

2/ Comprehensive Linguistic Annotation & Structured Transcriptions

Each recording included:

• Turn-by-turn segmentation
• Speaker identification
• Language labeling
• Timestamp synchronization
• Full transcriptions following client-specific Markdown format
• Metadata for topic, environment type, and audio characteristics

This structure enabled accurate model training for translation, diarization, and contextual speech understanding.

3/ High-Fidelity Audio Delivered in ML-Ready Format

All audio was delivered using:

• WAV container
• LINEAR16 encoding
• Mono channel
• 16-48 kHz sample rate
• Strict naming conventions
• Organized folder structure for ingestion workflows

These consistent parameters ensured seamless integration with the client’s machine learning pipelines.

• 780+ hours of natural bilingual conversation data delivered
• 60+ language pairs completed, including complex and low-resource combinations
• Major accuracy improvements in noisy and spontaneous speech conditions
• Better accent and dialect recognition, especially in Indic and multicultural environments
• Enhanced speech diarization and turn-taking accuracy
• Improved translation fluency across emotional, fast-paced, or overlapping speech
• Accelerated ML development cycles with fully structured, ready-to-train datasets
• Long-term partnership extending into new language pairs and recording campaigns