Voice AI systems combine several components working in sequence: automatic speech recognition converts spoken audio into text, natural language understanding identifies the caller's intent from that text, a response is generated, and text-to-speech converts the reply back into natural-sounding audio for the customer to hear. Accuracy at each stage compounds, so errors in speech recognition or dialect handling early in the pipeline directly reduce the quality of the final spoken response.
In Gulf markets, voice AI has to handle spoken Arabic dialects that vary noticeably from Modern Standard Arabic, as well as WhatsApp voice notes, which are a common way Gulf customers choose to communicate rather than typing. An AI concierge that only understands formal Arabic speech recognition will misinterpret a meaningful share of real customer voice input, making dialect-specific voice AI training essential for banking, retail, healthcare, and logistics deployments across the region.
In Eshal: Eshal's voice AI capability is built to handle Arabic dialect speech recognition alongside English, including WhatsApp voice notes, a widely used input format for Gulf customers who prefer speaking over typing. This allows the AI concierge to understand and respond to spoken customer enquiries with the same intent recognition and workflow automation used for text-based conversations.