I knew the high-level stuff but stumbled when they pushed on the specifics between wake word detection and the actual NLU pipeline.
Start by outlining the high-level stages of Alexa's voice processing pipeline, then dive into each stage with technical details and trade-offs. Emphasize the distributed, low-latency nature of the system and how components interact to deliver a seamless user experience.
Pro tip: Highlight the importance of latency and reliability at each step, and mention how Amazon uses edge computing and caching to minimize response time. Showing awareness of real-world constraints like network variability and device limitations will set you apart.
Explain how the device continuously listens for the wake word locally using on-device processing to preserve privacy and reduce latency. Mention techniques like keyword spotting and the trade-off between sensitivity and false positives.
Describe how the device captures the user's speech, encodes it (e.g., Opus), and streams it to the cloud over a persistent connection. Discuss challenges like packet loss, jitter, and the need for efficient compression.
Detail how the audio is transcribed to text using ASR models, then parsed to extract intent and entities via NLU. Mention the use of deep learning models and the need for scalability to handle millions of concurrent requests.
Explain how the intent is routed to the appropriate skill or service (e.g., music, weather) based on the user's request. Discuss how skills are invoked, and how they might call external APIs or internal microservices.
Describe how the response is generated (text-to-speech or audio stream) and sent back to the device for playback. Mention the role of caching, CDNs, and the need to handle errors gracefully.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.