Train enterprise-grade ASR, summarization, sentiment, and NLU models with diverse dual-channel, real-world call-center conversations across BFSI, Retail, Telecom, Healthcare, and more.
Delivered in 2–6 weeks with domain-specific dialogs, rich metadata, GDPR-aligned sourcing, and speaker diversity across 100+ languages/dialects, trusted by leading AI teams worldwide.
We’ve supported global AI teams with high-quality, multilingual call center speech datasets collected across real customer service scenarios in BFSI, Retail, Telecom, Healthcare, and more. Whether it’s agent QA, ASR training, or voice analytics, our data powers production-ready models around the world.
When someone calls a bank, a clinic, or a delivery helpline, they’re not calmly reading from a script. They’re speaking from a moving vehicle, a crowded living room, or a noisy office. They’re anxious, impatient, confused, or simply multitasking, and the way they speak reflects that.
This is the real test for your ASR, summarization, or voicebot model. Not the clean, perfect demo environment, but the messy, accented, emotional, overlapping conversations that happen every day in real call centers.
That’s what makes call center speech data so powerful. It doesn’t just teach your model how to transcribe; it teaches it how to listen, understand, and adapt in the real world.
But here’s the catch: most available datasets don’t come close. They’re scripted, single-speaker, overly clean, or worse, missing the spontaneity, noise conditions, and sentiment shifts that your system will face in production.
If your model isn’t trained on real conversations, it won’t survive real users. And that’s exactly what we help you fix from day one.
$213.5B Contact Center Software Market by 2032
✦
Global contact center software industry is set to quadruple in less than a decade, growing at a CAGR of 18.8%.
--Fortune Business Insights
55.4% of All Customer Interactions Are Still Inbound Voice
✦
Voice remains the primary contact method for customer support, and 57% of customer care leaders expect call volumes to rise.
--Call Centre Helper
1 in 3 Customers Leave After a Single Bad Experience
✦
One misheard phrase. One dropped call. One failed automation. That’s all it takes to lose a loyal customer.
--PwC
45.7% of Contact Centers Aren’t Tracking Emotion
✦
That’s nearly half of customer interactions happening without insight into tone, frustration, or sentiment, a missed opportunity for both AI and human teams.
--Call Centre Helper
You’ve optimized the model. Tuned the weights. Cleaned the transcripts. But performance still drops in real-world usage. Summarizations miss context. Sentiment detection feels flat. Diarization fails when the conversation gets messy.
And at some point, the question becomes clear: What kind of data is your system actually learning from?
These are the common roadblocks we hear from AI teams building speech solutions for real-world environments.
Many datasets mix both sides into a single channel. Without dual-channel audio, diarization, speaker adaptation, and real-time analytics degrade significantly.
Scripted, urban, accent-neutral voices cause overfitting to a narrow profile—and poor generalization to your real audience.
Studio-clean samples perform in tests but fail to generalize to moving vehicles, crowded offices, or noisy homes.
Support calls are emotionally charged, with feelings of frustration, urgency, hesitation, and relief. Without tonal variation, models miss intent and behavioral signals.
Missing or inconsistent labels (speaker roles, device type, intent, sentiment) impair downstream tasks and inflate cleanup cost.
A model trained on neat, noise-free samples might look great in early evaluations. But once it hits production traffic, accuracy drops sharply due to unseen accents, noise, or user behavior patterns.
it’s likely not the model, but the foundation. High-performing voice AI starts with speech data that reflects your users, not just the lab.
Your model isn’t just learning to transcribe speech; it’s learning to understand people in complex, fast-paced conversations. That’s why our approach starts with realism, structure, and intent.
From speaker diversity and dialog design to dual-channel audio and metadata tagging, we create datasets that reflect how real conversations actually unfold across languages, domains, emotions, and environments.
Collected using guided intent flows, not rigid scripts, ensuring conversations are realistic, unscripted, and aligned to actual use cases.
Agent and customer are captured on separate channels (dual-channel stereo) for reliable diarization, emotion analysis, and production-grade training.
Covers accents, age groups, and environments from quiet offices to real-world noise, to reflect how customers actually speak.
All transcriptions and labels are manually reviewed for accuracy, including speaker roles, intent, sentiment, and domain tagging.
Delivered with speaker tags, environment context, device info, and fully structured formats ready for direct model integration.
Delivered in 2–6 weeks with full QA, licensing, and documentation, built for production teams and scalable pipelines.
Whether you're training models for diarization, sentiment detection, intent classification, or summarization, we help you define exactly what your dataset needs, from call dynamics to data formats
✦
Inbound, outbound, or mixed call flows and topics
✦
Domain-specific scenario design (BFSI, Retail, Healthcare, etc.)
✦
Speaker quotas by gender, age, accent, device type
✦
Emotional tone balancing and sentiment class ratio control
✦
Sample rate, bit depth, audio format
✦
Dual-channel or single-channel delivery based on model pipeline
✦
Metadata fields customized to your architecture or schema
✦
File naming, directory structure, and output format tailored to your integration
Even the best architectures break when trained on mismatched data. Our datasets are designed to fix the failure points so your models perform where it matters..
✦
Improve WER across accents, disfluencies, and noisy conditions
✦
Strengthen diarization F1 during overlapping speech
✦
Boost emotion detection across tonal shifts and transitions
✦
Enhance summarization coherence in long, multi-intent calls
✦
Track goal shifts and multi-intent accuracy mid-dialogue
✦
Evaluate models with structured ground truth for benchmarking
✦
Run A/B tests against your existing datasets with parallel data
✦
Cover edge cases like accent drift, code-switching, and rare intents
Most teams focus on surface-level specs like language, speaker count, and audio format. But real-world accuracy isn’t built on spreadsheets. It’s shaped by the subtle, messy, human dynamics of real conversations.
These are the details that get ignored in most datasets, but not by your models.
Customers often switch between languages or blend accents mid-sentence. We preserve this natural drift to train models for multilingual and accented speech handling.
Calls rarely stay neutral. A frustrated tone softens, urgency builds. We tag emotional transitions so models learn to track tone across the call timeline.
Real calls are messy. Customers interrupt, agents clarify mid-response. Our stereo recordings preserve speaker overlap to train more robust diarization and ASR models.
“Umm… actually, I meant…” These self-corrections are frequent. We retain them, not remove them, so your model learns to handle uncertainty and corrections.
Real callers shift between goals mid-call. We capture and tag evolving intents, so your model adapts to natural dialog transitions.
We don’t stop at language and gender. Our metadata includes speaker role, sentiment, domain, background noise condition, and device type; every detail matters.
Hear from industry leaders who have transformed their AI models with our high-quality data solutions.
"We were struggling with poor ASR accuracy on real-world support calls, especially across Hindi and Tamil speakers. FutureBeeAI delivered dual-channel conversations that actually reflected our users, full of sentiment shifts, interruptions, and regional accents. The transcriptions were clean, metadata was reliable, and our model performance improved almost immediately after retraining"
"What stood out most was how structured the entire process was. We defined speaker quotas, call topics, and languages, and FutureBeeAI handled everything through their platform. Weekly check-ins, QA updates, and delivery milestones were always met. It never felt like outsourcing, more like working with an internal team."
Let’s create a call center dataset that mirrors your real users across the languages, emotions, domains, and environments that matter to your model.
Fast, accurate, structured, and fully transcribed. Designed for production from day one.
