Real-Time Voice Processing: From Speech to Insights in Milliseconds

Every millisecond counts in live interview assistance. Our voice processing pipeline transforms spoken questions into actionable insights faster than you can blink. It's an intricate dance of audio engineering, machine learning, and network optimization designed to deliver one thing: a seamless, real-time advantage.

The Challenge of Real-Time Voice Processing

Processing human speech in real-time presents unique engineering challenges that become critical in high-stakes interview scenarios. The system must be robust enough to handle ambient noise, different accents, and varying speech paces. More importantly, the end-to-end latency—from the moment a word is spoken to the moment an insight is displayed—must be imperceptible to the user. A delay of even half a second can break conversational flow and undermine confidence. Our challenge was to build a system that wasn't just fast, but felt instantaneous.

Pipeline Architecture Overview

Our voice processing pipeline consists of five optimized stages, each designed for minimal latency. By breaking the problem down, we can squeeze milliseconds out of every step, from the microphone on your device to our AI models and back. This multi-stage approach ensures both speed and accuracy.

Stage 1: Audio Capture & Preprocessing (~10ms)

The foundation of our pipeline begins with optimized audio capture on the client device. Raw audio is captured from the microphone at its native sample rate and immediately downsampled to 16kHz. This is the industry standard for high-accuracy speech recognition and significantly reduces the amount of data that needs to be transmitted. The audio is then encoded into a 16-bit PCM format. This entire process is executed in a low-level audio worklet, running in a separate thread from the main UI to prevent any interface lag.

Stage 2: Voice Activity Detection (VAD) (~5ms)

Constantly streaming audio, including silence, is inefficient and costly. We employ a lightweight, on-device Voice Activity Detection (VAD) model. This tiny machine learning model listens to the audio stream and makes a simple decision: is someone speaking or is this just background noise? Only audio chunks containing speech are sent to our backend. This drastically reduces network traffic and prevents our transcription models from processing unnecessary data, which is a key optimization for maintaining low latency.

Stage 3: Streaming Transcription (~50ms)

Once a speech segment is detected, it's streamed over a secure WebSocket to our Speech-to-Text (STT) engine. Unlike traditional transcription services that require a full audio file, our streaming STT provides real-time, partial transcripts as you speak. This means we don't have to wait for you to finish your sentence to start understanding the question. The transcription model is fine-tuned for interview contexts, improving its accuracy for technical jargon and common behavioral questions.

Stage 4: Context Analysis & Enhancement (~30ms)

A raw transcript is often not enough. This stage acts as a "Natural Language Understanding" (NLU) layer. The incoming transcript is enhanced with crucial context from your profile, including your resume summary, skills, and the job description for the role you're interviewing for. This layer identifies the core intent of the question, corrects any transcription errors based on context (e.g., distinguishing "Java" from "javascript"), and formats the query for optimal performance in the final AI stage.

Stage 5: AI Inference & Response Generation (~80ms)

The enhanced, contextualized query is then sent to our core AI model. This is not a general-purpose LLM like ChatGPT. It's a highly optimized, fine-tuned model designed for a single task: generating concise, glanceable talking points. The model's architecture is optimized for low-latency inference, and its output is deliberately brief to minimize the amount of text that needs to be sent back to your device. This focus on brevity is key to the "glance-and-say" experience that prevents you from breaking eye contact.

Conclusion

Our voice processing pipeline represents the convergence of audio engineering, machine learning, and distributed systems optimization. By meticulously refining each stage—from intelligent on-device processing to context-aware AI inference—we've engineered a system that delivers not just information, but confidence. This sub-200ms response time ensures that Hackly acts as a seamless extension of your own knowledge, providing the right talking points at the right moment, without ever breaking your conversational flow.