Most voice AI companies assemble third-party APIs. We train our own models. Every component of the Deylegen voice stack is optimized for African languages, African accents, and the specific demands of real-time financial services conversations.
01
Nigerian English Voice Synthesis
Our text-to-speech models are trained on Nigerian English speech patterns from multiple accent groups: Yoruba-inflected, Hausa-inflected, Igbo-inflected, and standard Nigerian English. The result is a voice that says sir and ma naturally, pronounces Nigerian names correctly, and speaks with the cadence and warmth your customers expect. Not American. Not British. Nigerian.
Trained on NVIDIA A100 GPUs | 2000+ hours of African English speech data
02
Real-Time Speech Recognition
Our speech-to-text models are fine-tuned for the acoustic realities of African phone calls: Nigerian English accents, Pidgin-inflected speech, code-switching between languages, GSM audio compression, and background noise from market environments. The model runs in real-time with intelligent end-of-turn detection, responding at the natural moment in conversation.
Optimized for Nigerian mobile network audio | Sub-300ms transcription latency
03
Sub-800ms Conversation Pipeline
From the moment a customer finishes speaking to the moment they hear a response: under 800 milliseconds. Speech recognition, language model reasoning with compliance guardrails, and voice synthesis execute in a parallelized pipeline on GPU-accelerated infrastructure. Faster than a natural conversational pause. Indistinguishable from a human agent.
Full pipeline on GPU infrastructure | Faster than human response time
INFRASTRUCTURE
Building foundational voice AI for African languages
Deylegen is not just an application layer. We are building the underlying voice AI infrastructure for African financial services. Our research and engineering work spans voice synthesis, speech recognition, and conversational AI, all optimized for African languages and deployed in production financial environments.
2000+
Hours of African English speech data in our training pipeline
10+
Nigerian accent groups represented in our voice models
<800ms
End-to-end conversation pipeline latency on GPU infrastructure
Production
Deployed and handling real customer calls in Nigerian financial institutions
Our models are trained on NVIDIA A100 GPUs using datasets including AfriSpeech-200 (200 hours of African speech, 142 hours Nigerian), proprietary banking conversation data, and custom-recorded financial services scripts. We are expanding to additional African languages including Yoruba, Hausa, and Igbo for multilingual banking agents.
Research focus areas: Nigerian English text-to-speech synthesis, accent-adaptive speech recognition for West African English, real-time voice agent orchestration, and containerized on-premise deployment for regulated industries.