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1 change: 0 additions & 1 deletion _typos.toml
Original file line number Diff line number Diff line change
Expand Up @@ -39,7 +39,6 @@ Similarily = "Similarily"
Simle = "Simle"
Sovler = "Sovler"
Successed = "Successed"
accoustic = "accoustic"
classfy = "classfy"
contxt = "contxt"
convertion = "convertion"
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2 changes: 1 addition & 1 deletion docs/design/network/deep_speech_2.md
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Expand Up @@ -102,7 +102,7 @@ Issue for each task will be created later. Contributions, discussions and commen

### Overview

Traditional **ASR** (Automatic Speech Recognition) pipelines require great human efforts devoted to elaborately tuning multiple hand-engineered components (e.g. audio feature design, accoustic model, pronuncation model and language model etc.). **Deep Speech 2** (**DS2**) \[[1](#references)\], however, trains such ASR models in an end-to-end manner, replacing most intermediate modules with only a single deep network architecture. With scaling up both the data and model sizes, DS2 achieves a very significant performance boost.
Traditional **ASR** (Automatic Speech Recognition) pipelines require great human efforts devoted to elaborately tuning multiple hand-engineered components (e.g. audio feature design, acoustic model, pronuncation model and language model etc.). **Deep Speech 2** (**DS2**) \[[1](#references)\], however, trains such ASR models in an end-to-end manner, replacing most intermediate modules with only a single deep network architecture. With scaling up both the data and model sizes, DS2 achieves a very significant performance boost.

Please read Deep Speech 2 \[[1](#references),[2](#references)\] paper for more background knowledge.

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