Dialect-Sensitive AI Voice Assistant
Dialect Accuracy
80%
Designed for Arabic-speaking markets, this AI voice assistant by SapientPro supports dialect recognition and cultural context awareness, delivering more natural, localized, and responsive user experiences across voice-first interactions.
Our Collaboration Story
Our client came to us with a clear idea: build an application that can recognize Arabic dialects from the first few words of a phone call. Once the system picks up the dialect, the AI switches to it and carries on the conversation accordingly.
To make this possible, we explored a range of existing models and technologies. After careful evaluation, we developed a solution that brings together several ready-made models, allowing the system to detect dialects reliably and carry on the interaction in a natural conversational flow.
Challenges
Dialect & Gender Detection
Needed to identify speaker’s dialect, gender, and tone to enable accurate, culturally relevant voice interactions.
Mixed-Language Handling
Required the assistant to process conversations in Arabic mixed with French or English without loss of context.
Contextual Dialogue Generation
Building prompts that reflect cultural, linguistic, and emotional cues from the speaker for more natural communication.
TTS Accuracy
Generating spoken responses with the right accent, inflection, and emotional tone, matching both dialect and intent.
Low-Latency Response
Voice recognition and response needed to happen in real time for uninterrupted conversations, even over VoIP.
Voice Assistant: Features Breakdown
Real-Time Dialect Detection
The assistant recognizes a user’s Arabic dialect from the initial speech segment, allowing for accurate regional adaptation from the start of the call.
Dynamic AI Response in Native Dialect
Once the dialect is identified, the AI adjusts its responses accordingly, matching vocabulary, phrasing, and intonation patterns common to that dialect.
Hybrid Model Integration
Multiple pre-trained language models are combined to improve recognition accuracy and reduce response time, balancing reliability with efficiency.
Cultural and Linguistic Awareness
The system considers gender, tone, and regional cues to shape more relatable, respectful conversations for users across different Arab-speaking communities.
Natural Voice Output
A customized TTS engine generates spoken replies with appropriate accents, cadence, and emotional tone, closely resembling native speech.
Low-Latency VoIP Connectivity
Built-in VoIP integration supports real-time communication, allowing voice input and output to flow without noticeable delay during calls.
Solutions
01
Linguistic Profiling Engine
Designed a model to detect dialect, gender, and tone dynamically, adjusting responses for cultural and regional accuracy.
02
Multilingual Parsing Logic
Built logic to recognize and respond to Arabic with embedded non-Arabic terms, preserving intent and conversational flow.
03
Cultural Prompt Design
Developed context-aware responses based on linguistic cues and regional sensitivities, increasing user comfort and engagement.
04
Natural-Sounding Voice Output
Implemented a TTS engine that mirrors native speech patterns, applying culturally appropriate pacing, accents, and emphasis.
05
VoIP-Integrated Architecture
Connected the assistant to VoIP systems, minimizing latency and maintaining real-time performance across audio sessions.
UNLEASH YOUR IDEA
Max Tatarchenko
CTO with 14 years of experience in solution architecture and engineering, specializing in blockchain and smart contracts. His broad expertise drives innovation across diverse technology projects.
CTO with 14 years of experience in solution architecture and engineering, specializing in blockchain and smart contracts. His broad expertise drives innovation across diverse technology projects.
UNLEASH YOUR IDEA
