Contact-Centre Speech Data Services for Pangeanic

The Client Need

Voice AI systems often perform well on clean samples but degrade with telephony compression, interruptions, overlapping speech, noise, accents, and mixed customer intents. Pangeanic needed a dataset service profile that reflected operational friction rather than idealised audio.

What NLPConsultancy Supplied

We engineered the speech data preparation across target scenarios, focusing on the chaos of contact-centre interactions:

• Telephony-style speech-data preparation for ASR, IVR, and voicebot evaluation workflows.
• Speaker-turn segmentation, diarisation support, and human-reviewed transcripts.
• PII-aware handling options, redaction workflows, and controlled reviewer access.
• Metadata tagging for channel, noise type, speaker role, language variety, and interaction type.

Dataset Characteristics

The delivered corpora featured intent, topic, or scenario labels to directly support Natural Language Understanding (NLU), routing, and agent-assist test harnesses. The data simulated the exact conditions systems face in production call centres.

Quality & Validation Process

Every transcript was human-reviewed to capture overlapping speech and dysfluencies. PII redaction rules were strictly enforced during the preparation stage to ensure the final datasets could be safely ingested into evaluation pipelines without exposing sensitive caller data.

Outcome for Pangeanic

• Pangeanic obtained a more realistic evaluation base for customer-service voice AI workflows.
• The metadata structure made it easier to diagnose errors by channel, noise, intent, language variety, or speaker role.
• Human-reviewed reference material supported model, vendor, and version comparisons.
• Controlled sourcing and processing reduced reliance on unsuitable public audio.