Production ASR Active Learning Loop from Contact Center Audio Logs
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About the Project
This active learning infrastructure was built for a multinational telecommunications provider operating high-volume contact centers that handle over 200,000 inbound customer calls every day.
Accurate speech-to-text transcripts were required for regulatory call compliance, agent QA scoring, and automated CRM summaries. However, off-the-shelf ASR engines struggled with regional dialects, specialized modem hardware models, and telephony audio compression.
Challenges We Faced
1. Telephony Bandwidth Degradation (8kHz Sampling Rates)
Customer calls are compressed over G.711 narrow-band codecs with an 8kHz sampling rate, heavy packet jitter, and background speaker bleed. Standard speech models trained on pristine 16kHz audio suffered a 32% Word Error Rate (WER) on technical brand and hardware terms.
2. The Labeling Needle in the Haystack
Manually transcribing 200,000 calls a day was financially impossible ($2M+ annual labeling cost). The system needed an automated mechanism to identify the specific 0.5% of audio snippets that the model was genuinely confused about, ignoring already-mastered dialogue.
3. Catastrophic Forgetting During Acoustic Fine-Tuning
Fine-tuning speech models exclusively on corrected technical mispronunciations caused the model to degrade on general conversational English, hallucinating technical jargon into mundane greetings.
Solution Architecture
Acadify engineered a continuous active learning loop powered by acoustic uncertainty sampling and elastic model consolidation, delivered under our operation automation workflows practice.
Instead of random audio sampling, our system tracks token-level log-probabilities across the ASR decoder beam. Audio slices with high entropy are automatically extracted and pushed to a streamlined human-in-the-loop verification queue.
- Uncertainty-Based Snippet Slicing: Monitored decoder log-probabilities in real time; whenever acoustic confidence dropped below threshold on specialized terminology, the exact 4-second audio slice was extracted and normalized using FFmpeg.
- Streamlined Annotation Interface: Built a focused React and WaveSurfer.js interface for call QA supervisors, reducing the time to verify and correct a 4-second audio slice to under 3 seconds.
- Elastic Weight Consolidation (EWC): Fine-tuned Whisper Large-v3 and Conformer-CTC models weekly using Elastic Weight Consolidation, penalizing changes to critical foundational weights to retain general English mastery while adapting to telephony acoustics.
Why Acadify's Engineering Approach Fit the Project
Maintaining production speech recognition is an ongoing operational discipline, not a one-time deployment. Acadify designed a closed-loop active learning system that reduced human transcription labor by 98% while directing labeling effort precisely where acoustic uncertainty was highest.
System Architecture & Tech Stack
Related Acadify Solution Services
This case study demonstrates capabilities from Acadify Solution's AI Development and Data Annotation Services practices.