I would like to analyze speech samples looking for speech-language pathologies. Most of the resources I can find are about speech recognition which is a completely different problem.
- I will always know what is the text that is being spoken on the given sample.
- I can assume that audio samples will always be of good quality with no background noises.
I would like to construct a neural net(or maybe some different model) that will detect certain anomalies, but I have few questions.
- How should I pass the input to the net? The input will have two parts: speech and text being spoken (+ label). Should I make I somehow divide these parts or just concatenate them as one sample?
- What can I read to better understand working with audio/speech in ML environment?