Loka developed a conversational AI agent using Amazon Nova 2 Sonic, improving customer engagement through natural, responsive voice interactions. The solution delivers faster response times and lower costs than traditional voice AI pipelines. AWS-based systems achieved high speech reasoning accuracy on Big Bench Audio while reducing latency and expenses.
Traditional voice assistants face delays due to a three-step process involving speech-to-text, language model processing, and text-to-speech conversion. This pipeline often introduces 3 to 5 second pauses, disrupting natural conversation flow. A real-world example at an automotive dealership showed how traditional systems struggle with complex queries, losing crucial information during speech-to-text conversion. The delays and misunderstandings negatively impact customer experience and increase support costs.
Native speech-to-speech models, like Amazon Nova 2 Sonic, process audio end-to-end, capturing tone, emotion, and subtle cues missed by traditional text-only pipelines. Rigorous testing confirmed the model's effectiveness, achieving a speech reasoning score of 87.0 on Big Bench Audio. This outperformed Gemini 2.5 Flash Native Audio (Live API) at 71.0 and exceeded GPT Realtime’s 83.0. The model also achieved a Time to First Audio of 1.39 seconds, enabling natural 'barge-in' behavior during conversations.
Source: awsml
