{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/listen-attend-and-spell","title":"Listen, Attend and Spell","arxiv_id":"1508.01211","date":"2015-08-05","proceeding":null,"authors":["William Chan","Navdeep Jaitly","Quoc V. Le","Oriol Vinyals"],"abstract":"We present Listen, Attend and Spell (LAS), a neural network that learns to\ntranscribe speech utterances to characters. Unlike traditional DNN-HMM models,\nthis model learns all the components of a speech recognizer jointly. Our system\nhas two components: a listener and a speller. The listener is a pyramidal\nrecurrent network encoder that accepts filter bank spectra as inputs. The\nspeller is an attention-based recurrent network decoder that emits characters\nas outputs. The network produces character sequences without making any\nindependence assumptions between the characters. This is the key improvement of\nLAS over previous end-to-end CTC models. 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