UK Customer-Ops Platform ASR Fine-Tuning
See how Muoro fine-tuned speech recognition for UK voice bots, improving postcode recognition accuracy from 80% to 97% and reducing word error rate to 3%.
Business Outcomes
Business Outcomes
80% → 97%
Recognition accuracy on postcodes
20% → ~3%
Word Error Rate, measured
Live
In production voice botsAbout Client
The client runs a UK customer-operations platform built around production voice bots that authenticate callers using postcodes. Because postcode recognition is a hard authentication requirement, any failure in speech recognition directly translated into failed customer verification and service friction.Platform Engineering
The Challenge
Formats like 'SW1A 1AA' confused base speech models.
The Challenge
Formats like 'SW1A 1AA' confused base speech models.
Phonetic Inconsistency
Phonetic spelling conventions were handled inconsistently.Noise & Accents
Accents and background noise dropped recognition reliability.Hard Requirement
Postcode recognition was a hard authentication requirement, not optional.Phonetic Inconsistency
Phonetic spelling conventions were handled inconsistently.Noise & Accents
Accents and background noise dropped recognition reliability.Hard Requirement
Postcode recognition was a hard authentication requirement, not optional.Phonetic Inconsistency
Phonetic spelling conventions were handled inconsistently.Noise & Accents
Accents and background noise dropped recognition reliability.Hard Requirement
Postcode recognition was a hard authentication requirement, not optional.Phonetic Inconsistency
Phonetic spelling conventions were handled inconsistently.Noise & Accents
Accents and background noise dropped recognition reliability.Hard Requirement
Postcode recognition was a hard authentication requirement, not optional.The Muoro Solution
Azure STT was fine-tuned on thousands of recorded and simulated utterances.
The Muoro Solution
Azure STT was fine-tuned on thousands of recorded and simulated utterances.
Fine-Tuned STT
Azure STT was fine-tuned on thousands of recorded and simulated utterances.Noise Simulation
Real-call noise and quality degradation were simulated in training.Custom Dictionaries
Custom pronunciation dictionaries and domain language models were built.Azure Speech-to-Text
|Custom Pronunciation Dictionaries
|Domain Language Models
|Post-Processing Layer
Impact & Results
Recognition accuracy rose from 80% to 97%.
Impact & Results
Recognition accuracy rose from 80% to 97%.
WER Cut to ~3%
Word error rate on postcodes fell from 20% to roughly 3%.Less Manual Verification
Manual verification load was significantly reduced.Live in Production
The model is integrated and live in production voice bots.WER Cut to ~3%
Word error rate on postcodes fell from 20% to roughly 3%.Less Manual Verification
Manual verification load was significantly reduced.Live in Production
The model is integrated and live in production voice bots.WER Cut to ~3%
Word error rate on postcodes fell from 20% to roughly 3%.Less Manual Verification
Manual verification load was significantly reduced.Live in Production
The model is integrated and live in production voice bots.Final Outcome
UK postcode recognition on live voice bots: 80% → 97%. Word error rate: 20% → 3%.
Final Outcome
UK postcode recognition on live voice bots: 80% → 97%. Word error rate: 20% → 3%.
Improve speech recognition for production voice AI
Fine-tune speech recognition models for higher accuracy, fewer verification failures, and reliable performance in real world voice applications.
Improve speech recognition for production voice AI
Fine-tune speech recognition models for higher accuracy, fewer verification failures, and reliable performance in real world voice applications.
No challenge is too complex for our team to solve
Please share your requirements with us and our experts will get back to you within 24 hours.
BOOK A STRATEGY CALL