Voice & Speech AI implementation checklist

Verify that the audio capture sample rate matches the model requirements (typically 16kHz for STT) to avoid resampling artifacts.

Ensure Acoustic Echo Cancellation (AEC) is enabled in the browser or OS to prevent the AI's output from being captured as new input.

Implement local VAD to stop transmission during silence, reducing unnecessary API costs and processing load.

Create explicit UI states for 'denied', 'blocked', and 'no device found' to guide users through hardware troubleshooting.

Apply a gain stage to normalize input levels, preventing clipping from loud speakers or inaudible low-volume inputs.

Replace REST polling with full-duplex streaming protocols to minimize the overhead of repeated HTTP handshakes.

Display interim results to the user immediately rather than waiting for the final finalized transcript block.

Tune audio buffer chunks (e.g., 100ms to 250ms) to find the balance between network overhead and processing speed.

Configure the Text-to-Speech provider to stream audio bytes so playback begins before the entire sentence is synthesized.

Deploy STT/TTS models in regions closest to the end-user to minimize Round Trip Time (RTT).

Supply a list of industry-specific terms, product names, or acronyms to the API to improve recognition of niche jargon.

Enable speaker labels if the use case involves multiple participants to ensure correct turn-taking attribution.

Configure server-side filters to redact sensitive information or inappropriate language before data reaches the application layer.

Test the system's ability to switch languages or handle code-switching if the target audience is multilingual.

Validate WER (Word Error Rate) in environments with 60dB+ of ambient noise (e.g., street noise, office chatter).

Use Speech Synthesis Markup Language (SSML) to control prosody, emphasis, and pronunciation of specific terms.

Use compressed formats like Opus or MP3 for delivery over mobile networks to reduce data consumption.

Implement a jitter buffer for synthesized audio to prevent gaps or 'pops' during network fluctuations.

Store pre-rendered audio files for common UI prompts (e.g., 'Hello', 'Goodbye') to eliminate API costs and latency.

Define a secondary voice provider or a local Web Speech API fallback in case the primary TTS service fails.

Implement a pipeline to periodically compare system transcripts against human-verified ground truth to track accuracy over time.

Set up automated alerts at 50%, 75%, and 90% of the monthly budget to prevent service suspension.

Implement ping/pong frames in WebSockets to detect and recover from silent network drops within 5 seconds.

Pass unique IDs through the entire audio pipeline (client -> STT -> LLM -> TTS) for debugging specific failed interactions.

Provide a simple 'thumbs up/down' UI for transcription quality to identify edge cases where the model fails.