I've been wondering why it's important, when collecting speech data for AI machine learning, to collect pre and post silence for speech data? Is it just so the machine learns and understands the difference between "silence", "background noise", and actual "speech"?
1 Answer
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Silence is often useful when segmenting the raw data into suitably sized samples for the machine learning methods. It is practical to run recording for several minutes at a time, but often the input to the machine learning model should for example be 1 second clips. Each clip should avoid cutting of a word midways. Having silence allows to segment words or phrases using a simple threshold algorithm.
