Understanding the level of an AI system can help predict how the system will change over time – whether it will continuously improve, remain the same, or even degrade. Read more

End-to-end architecture improves the scalability and performance of machine learning models for speech transcription to serve enterprise contact center needs better. Read more

How do you identify the right workflows to automate in the contact center? And quickly implement them? ASAPP was awarded a patent for the technology to meet this challenge. Read more

On the path to real-time speech sentiment analysis, new ASAPP research achieves training efficiency gains with transfer learning between spoken and written language domains. Read more

Rather than maintaining multiple ASR models that work under varying time constraints or conditions, new ASAPP research introduces a single multi-mode model that can dynamically adjust to different scenarios. Read more

In pursuit of our mission to enhance human performance and automate the world’s workflows, ASAPP is releasing one of the largest annotated datasets for clinical NLP. Read more

Highly accurate speech-to-text transcription can provide tremendous value, but most off-the-shelf ASR models struggle with the words that matter most. Read more

Dialogue State Tracking has run its course. That’s why we’re establishing a new Action-Based Conversations Dataset. Read more

Customers don’t like to wait. That’s no surprise. But, did you know keeping them waiting impacts CSAT scores, loyalty, and lifetime value? Read more

Building on recent advances in natural language processing, new research from Felix Wu identifies ways to significantly stabilize BERT fine-tuning on small datasets. Read more