Your voice pipeline is losing words. We find every one.
We audit enterprise voice pipelines — ASR accuracy, acoustic models, latency under load — and rebuild every layer until dialect dropout, misfire transcriptions, and IVR misroutes become engineering history.
Generic engines fail on the accents your users actually speak.
Click a dialect below. Watch the accuracy gap between a default ASR API and a tuned acoustic model built for that speaker population.
Appalachian English
"Patient reports fixin' to take two milligrams of metformin come evenin'."
Vowel shifts and dropped auxiliaries cause generic models to misfire on dosage terms — "fixin' to" drops entirely, "evenin'" transcribes as "evening" with altered context.
100% of utterances misfire or partially fail
+0pp improvement · acoustic model retrained on target population
Your pipeline looks fine at 100 streams. It breaks at 5,000.
Drag the slider to simulate concurrent audio streams. Watch p95 latency bend, break, then flatten after Waveform optimization.
Every layer of your stack has a different failure mode. We map all of them.
Scroll through the architecture. Each layer represents a distinct engineering problem your current vendor's dashboard doesn't surface.
Single Utterance
Acoustic Model LayerDefault acoustic models trained on clean studio speech fail on real-world microphone conditions — background noise, channel artifacts, codec compression at 8kHz.
Domain-specific acoustic model retraining on matched conditions. G2P rule injection for medical/automotive/financial terminology. Noise-robust feature extraction.
Continuous Dictation
Language Model LayerN-gram and transformer LMs trained on generic corpora assign low probability to domain vocabulary — "metformin" transcribes as "met for men," "IVR" becomes "I.V.R." with wrong tokenization.
Custom language model interpolation with domain corpus. Vocabulary expansion with pronunciation lexicon entries. Rescoring with BERT-based contextual reranker.
Multi-Speaker Diarization
Speaker Separation LayerCall center recordings with overlapping speech, hold music injection, and agent/customer channel bleed cause speaker attribution to collapse — 23% of turns misattributed in standard configs.
Speaker-conditioned acoustic scoring with x-vector embeddings. Overlap-aware segmentation with energy-based VAD. Post-processing diarization refinement with transcript-aligned clustering.
Real-Time Translation
End-to-End Pipeline LayerCascaded ASR → MT pipelines accumulate error at each boundary. A 6% ASR WER compounds into 19% semantic error after translation, making real-time multilingual IVR operationally unusable.
End-to-end speech translation with joint acoustic-semantic training. Streaming beam search with partial hypothesis commitment. Latency-accuracy tradeoff tuning per SLA requirement.
Download the Accuracy Gap Report
47 pages. WER benchmarks across 18 accent groups, latency profiles for 6 major ASR vendors, and a scoring rubric you can apply to your own pipeline today.
Here's what the audit changes. In numbers.
Ranges derived from 34 enterprise engagements across telehealth, automotive, and call center verticals. Your mileage will vary — the audit quantifies exactly how much.
* RANGES REPRESENT 25TH–75TH PERCENTILE OUTCOMES ACROSS 34 ENTERPRISE ENGAGEMENTS (2022–2025). INDIVIDUAL RESULTS DEPEND ON CURRENT STACK, DATA AVAILABILITY, AND DEPLOYMENT CONSTRAINTS.
Your pipeline has a number.
Let's find it.
A pipeline audit takes 5 business days. You receive a full diagnostic report — WER by dialect, latency profile, cost model, and a prioritized remediation roadmap. No retainer required to start.
Intake call. We review your current stack, SLA requirements, and top failure modes. You share sample audio (anonymized is fine).
Diagnostic run. We process your samples through benchmark suite — WER by dialect, latency under simulated load, diarization accuracy.
Analysis. We map failure modes to architecture layers and model the remediation cost vs. improvement curve.
Readout. You receive the full report plus a prioritized remediation roadmap with effort estimates and projected metric improvements.
"We were at 22% WER on patient intake calls. After the Waveform audit we understood exactly why — and had a remediation plan with projected outcomes before we'd spent a dollar on implementation."