Low-Latency Streaming ASR with Client-Side Keystroke Correction Feedback
Client identifiers anonymized under strict Non-Disclosure Agreements (NDAs). Performance metrics reflect architectural benchmark simulations.
About the Project
This real-time speech engine was engineered for a clinical dictation software provider whose mobile and desktop applications enable physicians to dictate patient consultation notes directly into electronic health record (EHR) systems.
Physicians expect instant, real-time transcription on screen as they speak (sub-200ms latency) and have zero tolerance for misrecognized drug dosages, pharmaceutical trade names, or anatomical descriptions.
Challenges We Faced
1. The Latency vs. Acoustic Context Dilemma
High-accuracy ASR models typically look ahead across subsequent audio frames to disambiguate homophones ('patient has two fractures' vs 'patient has to fracture'). Looking ahead introduces 600ms+ of lag, which physicians perceive as sluggish and disruptive to their speaking cadence.
2. Rapid Emergence of Specialized Pharmaceutical Nomenclature
New medications, generic chemical compounds, and surgical implants enter clinical practice monthly. Static acoustic models consistently misrecognized unfamiliar pharmaceutical names as common conversational words.
3. Capturing Implicit Correction Signals Without Disrupting Dictation
When a physician notices a misrecognized term, they backspace and type the correct word manually. Capturing this explicit correction event and feeding it back into the model's vocabulary needed to occur seamlessly without halting the active WebSocket stream.
Solution Architecture
Acadify engineered a low-latency chunked streaming ASR pipeline with on-the-fly WFST vocabulary biasing, delivered through our custom AI development services practice.
We deployed a causal Conformer-RNNT architecture operating on 160ms audio chunks over bidirectional WebSockets, coupled with an implicit client-side keystroke alignment listener that transforms everyday physician edits into training signals.
- Chunked Causal Streaming Architecture: Implemented a causal Conformer Transducer running on NVIDIA Triton Inference Server, processing 160ms audio frames with an end-to-end median latency of 180ms from microphone to screen.
- Client-Side Keystroke Telemetry Listener: Engineered an unobtrusive Electron desktop hook that detects when a user backspaces over an ASR-generated word and types a replacement, pairing the raw audio chunk with the user's manual correction as a training pair.
- Dynamic Runtime Hotword Biasing: Integrated an on-the-fly Weighted Finite-State Transducer (WFST) rescoring layer that injects the physician's medical specialty lexicon and recent patient chart medications directly into the decoder beam at session startup.
Why Acadify's Engineering Approach Fit the Project
Real-time streaming speech requires deep expertise in low-latency systems, audio frame chunking, and runtime decoder mechanics. Acadify delivered a production dictation engine with sub-200ms response times while turning physician keystroke corrections into an automated model adaptation loop.
System Architecture & Tech Stack
Related Acadify Solution Services
This case study demonstrates capabilities from Acadify Solution's AI Development practice, specifically real-time speech recognition and model adaptation.