{"action":"opened","issue":{"url":"https://api.github.com/repos/kaldi-asr/kaldi/issues/2256","repository_url":"https://api.github.com/repos/kaldi-asr/kaldi","labels_url":"https://api.github.com/repos/kaldi-asr/kaldi/issues/2256/labels{/name}","comments_url":"https://api.github.com/repos/kaldi-asr/kaldi/issues/2256/comments","events_url":"https://api.github.com/repos/kaldi-asr/kaldi/issues/2256/events","html_url":"https://github.com/kaldi-asr/kaldi/issues/2256","id":302630330,"number":2256,"title":"End-to-end training in Kaldi","user":{"login":"cocoakeith","id":12630218,"avatar_url":"https://avatars1.githubusercontent.com/u/12630218?v=4","gravatar_id":"","url":"https://api.github.com/users/cocoakeith","html_url":"https://github.com/cocoakeith","followers_url":"https://api.github.com/users/cocoakeith/followers","following_url":"https://api.github.com/users/cocoakeith/following{/other_user}","gists_url":"https://api.github.com/users/cocoakeith/gists{/gist_id}","starred_url":"https://api.github.com/users/cocoakeith/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/cocoakeith/subscriptions","organizations_url":"https://api.github.com/users/cocoakeith/orgs","repos_url":"https://api.github.com/users/cocoakeith/repos","events_url":"https://api.github.com/users/cocoakeith/events{/privacy}","received_events_url":"https://api.github.com/users/cocoakeith/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-03-06T09:47:34Z","updated_at":"2018-03-06T09:47:34Z","closed_at":null,"author_association":"NONE","body":"Hi\r\nI read the 'TOWARDS DISCRIMINATIVELY-TRAINED HMM-BASED END-TO-END MODELS FOR AUTOMATIC SPEECH RECOGNITION' recently, and I am glad to find that the example scripts have been merged into kaldi-master today. This paper proposes a simple yet effective approach to model left biphones(bichars) by creating a trivial full biphone tree, I have question regard to this approach. When training EE-LF-MMI for an language whose number of phonemes is 280 or more, the number of context dependent phones is 280*280*2=156800, it is too large. Is there any approach to reduce this number?\r\n\r\n"}}
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