Listen Attend Spell

• Listen, Attend and Spell
• LAS
• learns to transcribe speech utterances to characters
• nlike traditional DNN-HMM models, this model learns all the components of a speech recognizer jointly
• sequence-to-sequence framework
• trained end-to-end and has two main components: a listener (encoder) and a speller (decoder)
• listener is a pyramidal RNN encoder that accepts filter bank spectra as inputs, transforms the input sequence into a high level feature representation and reduces the number of timesteps that the decoder has to attend to.
• The speller is an Attention-based RNN decoder that attends to the high level Features and spells out the transcript one character at a time
• The proposed system does not use the concepts of phonemes, nor does it rely on pronunciation dictionaries or HMMs
• bypass the Conditional Independence assumptions of CTC, and show how they can learn an implicit language model that can generate multiple spelling variants given the same acoustics
• producing character sequences without making any independence assumptions between the characters is the key improvement of LAS over previous end-to-end CTC models
• used samples from the Softmax classifier in the decoder as inputs to the next step prediction during training
• show how a language model trained on additional text can be used to rerank their top hypotheses
• Google voice search task