Extracting Features from Multiple Audio Channels with Kaldi – NVIDIA Technical Blog News and tutorials for developers, data scientists, and IT admins 2025-03-27T16:00:00Z http://www.open-lab.net/blog/feed/ Levi Barnes <![CDATA[Extracting Features from Multiple Audio Channels with Kaldi]]> http://www.open-lab.net/blog/?p=19854 2022-08-21T23:40:35Z 2020-08-20T23:23:59Z In automatic speech recognition (ASR), one widely used method combines traditional machine learning with deep learning. In ASR flows of this type, audio...]]> In automatic speech recognition (ASR), one widely used method combines traditional machine learning with deep learning. In ASR flows of this type, audio...Extracting Features from Multiple Audio Channels with Kaldi

In automatic speech recognition (ASR), one widely used method combines traditional machine learning with deep learning. In ASR flows of this type, audio features are first extracted from the raw audio. Features are then passed into an acoustic model. The acoustic model is a neural net trained on transcribed data to extract phoneme probabilities from the features. A phoneme is a single��

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