Conversational AI Training
Breaking language barriers: orchestrating a global collection of 1M+ voice samples across 25+ dialects.
The Challenge
A global tech giant was building a next-generation multilingual conversational AI. Their existing speech models suffered from high word error rates in non-Western dialects and struggled heavily with natural conversational overlapping and background noise.
Our Solution
Dserve AI managed a massive worldwide data collection campaign. We recruited thousands of native speakers across 25+ languages and dialects. We recorded unscripted, naturalistic conversations, commands, and queries in varied acoustic environments (cars, cafes, streets). All audio was subsequently transcribed, speaker-diarized, and tagged with emotion and intent labels.
The Impact
"The dataset provided the necessary acoustic and linguistic diversity to train a truly global model. The client achieved state-of-the-art word error rates, seeing a 55% improvement in understanding colloquial queries across 12 of their most challenging target languages."
Global Acoustic Capture
01. Recruitment
Sourcing native speakers across 50 distinct dialects balancing age and gender demographics.
02. Environment Sim
Recording scripts in simulated acoustic environments like cars, cafes, and windy streets.
03. Diarization
Speaker separation and timestamping for overlapping conversational multi-party speech.
04. Intent Tagging
Applying semantic intent and emotion labels to the transcribed corpora.
The Code-Switching Challenge
Modern global citizens rarely speak a single language purely; they code-switch. We explicitly constructed scenarios where speakers naturally blended Hindi and English (Hinglish) or Spanish and English (Spanglish). By annotating these fluid transitions at the phoneme level, the client's NLU model learned to maintain conversational context without crashing when the language abruptly shifted mid-sentence.