5 ms·

Don't you need to have domain expertise anyway to engineer your features? What exactly will you train your speech recognition models on, if you have no understa

• PeterisP 9y ago
  With modern deep learning methods, you often can get state of art results without any feature engineering whatsoever - you do need a decent efficient representation of the raw data, but that's about it. That's the whole point, in many domains the accumulated knowledge about feature engineering isn't necessary anymore, since you can train a deep network to learn the same features implicitly from data, and (depending on the problem) it's quite possible that the learned features in the initial layers will be better than anything people have engineered before. For your speech example, speech recognition models used to contain explicit phonetics systems (where the chosen set of phonemes mattered), separating acoustic models and language models. But now you can also get decent results from an end-to-end system, by throwing all the phonetic knowledge you had into the trash bin and training an end-to-end model from the sound data (though, not a waveform but a frequency domain representation, e.g. https://en.wikipedia.org/wiki/Mel-frequency_cepstrum https://en.wikipedia.org/wiki/Mel-frequency_cepstrum - but that's not "understanding of speech", it's understanding of audio data format) straight to the output text characters.
  • deleted 9y ago
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