Ensemble-based Machine Learning Characterization Of Human-machine Dialog
- Author(s):
- Ramanarayanan, Vikram; Mulholland, Matthew; Ghosh, Debanjan
- Patent Issued:
- Jan 02, 2024
- Patent Number:
- 11,861,317
- Source:
- ETS Patent
- Document Type:
- Patent
- Family ID:
- 1000005595813
- Subject/Key Words:
- Patent, Active Patent, Dialogues, Automatic Speech Recognition, Machine Learning, Computer-Implemented, Performance Assessment
Abstract
Human-machine dialog is characterized by receiving data comprising a recording of an individual interacting with a dialog application simulating a conversation. Thereafter, the received data is parsed using automated speech recognition to result in text comprising a plurality of words. Features are extracted from the parsed data and then input an ensemble of different machine learning models each trained to generate a score characterizing a plurality of different dialog constructs. Thereafter, scores generated by the machine learning models for each of the dialog constructs are fused. A performance score is then generated based on the fused scores which characterizes a conversational proficiency of the individual interacting with the dialog application. Data can then be provided which includes or otherwise characterizes the generated score. Related apparatus, systems, techniques and articles are also described.