Raven-1

Velma is a voice-native AI model developed by Modulate as part of a broader voice intelligence platform, designed to understand conversations directly from audio rather than relying on text transcripts. Unlike traditional systems that convert speech into text and analyze it with language models, Velma uses an Ensemble Listening Model (ELM), a specialized architecture that processes multiple dimensions of voice simultaneously, including tone, emotion, pacing, intent, and behavioral signals. This allows it to capture the full meaning of a conversation, not just the words spoken, recognizing nuances such as stress, deception, sarcasm, or escalation in real time. It operates by combining hundreds of specialized detectors, each focused on specific aspects of speech like emotional state, inappropriate conduct, or synthetic voice indicators, and then fusing those signals into higher-level insights about what is happening in a conversation.

Higgs Audio / Avatar is a family of foundation audio and avatar models designed to generate natural speech, understand tone, emotion, and intent, and give voice interactions a visual presence. The models support text-to-speech, speech-to-text, avatar generation, and automatic voice casting that selects an appropriate voice based on context, sentiment, and content. Built for real-world production, Higgs combines expressive generation, robust speech understanding, and flexible deployment for workloads where quality, latency, and reliability matter. High-accuracy multilingual speech recognition supports major languages, while voice cloning reproduces a speaker’s tone from short reference samples to maintain consistent brand voices across interactions. Sentiment detection reads emotional signals in speech to enable smarter routing, stronger analytics, and more context-aware agent behavior.