{"podcast":{"title":"AI Engineering Podcast","slug":"ai-engineering-podcast","podcast_index_feed_id":5875646,"rss_url":"https://serve.podhome.fm/rss/c9abdd38-a5dc-5eb2-96fd-f833f93208a7","website_url":"https://www.aiengineeringpodcast.com","image_url":"https://assets.podhome.fm/f6ff0caa-931b-4c08-bfdd-08dc7f5cd336/638557211890591941ai_engineering_podcast_logo.jpg","author":"Tobias Macey","episode_count":79,"summary":"This show is your guidebook to building scalable and maintainable AI systems. You will learn how to architect AI applications, apply AI to your work, and the considerations involved in building or customizing new models. Everything that you need to know to deliver real impact and value with machine learning and artificial intelligence.","last_synced_at":null,"page_url":"https://stenobird.com/podcast/ai-engineering-podcast"},"episode":{"title":"Taming Voice Complexity with Dynamic Ensembles at Modulate","slug":"taming-voice-complexity-with-dynamic-ensembles-at-modulate","published_at":"2026-02-08T21:03:07+00:00","page_url":"https://stenobird.com/podcast/ai-engineering-podcast/taming-voice-complexity-with-dynamic-ensembles-at-modulate","show_page_url":"https://stenobird.com/podcast/ai-engineering-podcast","url":"https://www.aiengineeringpodcast.com/ensemble-listening-models-episode-76","audio_url":"https://op3.dev/e/dts.podtrac.com/redirect.mp3/serve.podhome.fm/episode/f6ff0caa-931b-4c08-bfdd-08dc7f5cd336/63906178161892160583ed2644-e8ca-4e05-bf77-bba64fd20392.mp3","summary":"Carter Huffman, CTO of Modulate, explains how to move beyond simple speech-to-text pipelines using Ensemble Listening Models (ELMs). He details how dynamic routing and small model ensembles can capture non-textual signals like emotion and tone with high efficiency.","meta_description":"Learn how Modulate uses Ensemble Listening Models (ELMs) to solve the complexity of low-latency, high-accuracy Voice AI through dynamic model routing.","key_points":["Main idea: Voice AI requires capturing non-textual signals like tone and emotion that standard text-based LLMs often miss","Practical takeaway: Use ensembles of small, specialized models for repetitive, structured tasks to achieve better cost-efficiency and scalability than large foundation models","Failure mode: Monitoring only the text output of a voice bot creates a blind spot for errors occurring in the audio or text-to-speech layers","Architecture insight: Modulate's ELM uses dynamic routing and cost-based optimization to balance accuracy and latency","Engineering lesson: Complex distributed AI systems require advanced observability and automated red-teaming to catch unpredictable out-of-distribution behaviors"],"chapters":[{"start_ms":350000,"title":"The Unique Challenges of Voice AI","summary":"Why voice is a harder modality than text or video due to the nuanced, non-verbal signals like emotion and context."},{"start_ms":595000,"title":"Architecture of Ensemble Listening Models","summary":"An exploration of using specialized models to address accuracy issues found in quantized or smaller models."},{"start_ms":860000,"title":"From Static to Dynamic Ensembles","summary":"The evolution of Modulate's architecture from static ensembles to more intelligent, adaptive routing."},{"start_ms":1855000,"title":"Scaling Small Models for Structured Tasks","summary":"Why ensembles of small models are ideal for tasks with shared properties, such as analyzing conversation demographics and intent."},{"start_ms":2255000,"title":"Handling Long-Horizon Context","summary":"Strategies for managing memory and retrieval when analyzing long-duration monologues or conversations."},{"start_ms":2535000,"title":"Distributed Systems and Complexity","summary":"The engineering overhead of running ensemble architectures across distributed neural network components."},{"start_ms":3300000,"title":"The Future of AI Observability","summary":"Identifying the gaps in current monitoring tools and the need for automated red-teaming in complex AI pipelines."}],"topics":["Voice AI","Ensemble Learning","Machine Learning Engineering","Low-latency Inference","Model Observability","Distributed Systems","Audio Signal Processing","Cost Optimization"],"duration_seconds":3565,"processing_state":"processed","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/ai-engineering-podcast/episodes/taming-voice-complexity-with-dynamic-ensembles-at-modulate/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/ai-engineering-podcast/taming-voice-complexity-with-dynamic-ensembles-at-modulate.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}