Speech systems note
Audio Is Computer Vision
A spectrogram is a picture of sound: time on one axis, frequency on the other, color as energy.
Spectrograms
Convolutions
Speech Recognition
Video Analysis
Once audio is drawn as a spectrogram, the analogy to computer vision becomes very direct.
- Image classification: pixels -> convolution or encoder -> label.
- Audio classification: spectrogram -> convolution or encoder -> label.
- Speech recognition: spectrogram over time -> encoder/decoder -> word sequence.
- Video analysis: frames over time -> image encoder plus temporal model -> action or event.
The common move is learning local patterns on a grid. In images those patterns are edges, textures, and parts. In spectrograms they are bursts, harmonics, formants, silence, and other time-frequency shapes.
Speech recognition and video analysis then add the same extra problem: order over time. You are not just classifying one grid; you are reading a sequence.
Plainly: the analogy is not perfect, but it is the first mental model I use when explaining speech recognition to someone who already understands vision.