Fluent Speech Commands: A dataset for spoken language understanding research

In recent years, with the advent of deep neural networks, the accuracy of speech recognition models have been notably improved which have made possible the production of speech-to-text systems that can accurately transcribe speech even in difficult scenarios such as noisy environments, spontaneous speech or high variability (speaking rate, accents, etc.). However, spoken language understanding (SLU) is still an open problem. Modern systems are far from being able to correctly interpret the meaning of the words uttered by a user unless the domain is highly constrained.