Building competitive direct acoustics-to-word models for English conversational speech recognition

Understand

Direct acoustics-to-word (A2W) models in the end-to-end paradigm have received increasing attention compared to conventional sub-word based automatic speech recognition models using phones, characters, or context-dependent hidden Markov model states.

• This is because A2W models recognize words from speech without any decoder, pronunciation lexicon, or externally-trained language model, making training and decoding with such models simple.
• Prior work has shown that A2W models require orders of magnitude more training data in order to perform comparably to conventional models.
• Our work also showed this accuracy gap when using the English Switchboard-Fisher data set.