Hundreds of Thousands of Hours
of Real Call Center Audio

AIxBlock offers off-the-shelf call center audio datasets consisting of hundreds of thousands of hours of real customer–agent conversations. These datasets are ready to license and are used to train and benchmark ASR and voice AI systems on real production audio, not scripted speech.

AIxBlock’s off-the-shelf call center audio datasets contain real, unscripted calls with background noise, interruptions, and accent variation. This distinguishes them from synthetic or studio-recorded datasets that often fail to represent how ASR models behave in live contact center environments.

AIxBlock’s OTS audio library includes English with US, Indian, and Philippine accents, along with multiple Indian languages as well as some European languages. This coverage reflects large global call center markets where accent diversity and noisy conditions significantly impact ASR accuracy.

Teams use AIxBlock’s off-the-shelf call center audio datasets for ASR training, word error rate benchmarking, speaker diarization testing, and voicebot evaluation. These datasets are commonly used by voice AI platforms and contact center analytics teams targeting production reliability.

AIxBlock’s off-the-shelf call center audio datasets are designed for rapid licensing. Teams can typically begin training or evaluation within days, avoiding the months required for custom speech collection and annotation projects.

Yes. AIxBlock can provide optional transcription and annotation for off-the-shelf call center audio datasets upon request. This allows ASR teams to move directly without engaging a separate annotation provider.

We take compliance seriously. The audio is processed to redact sensitive PII (such as credit card numbers or full names) where necessary, ensuring you can train on real human dialogue without violating data privacy standards.

Yes. We understand that engineering teams need to validate the data format and acoustic characteristics. We offer pilot samples so you can run an initial evaluation against your current model benchmarks before committing to a bulk license.