I have to make a live speech recognition program that can spot specific words in a spoken sentence. For now I have to recognize the words "yes" and "no".
I already trained Google's model and it worked, but only if the words were spoken apart. (The dataset they provide contains wav files of 1 second each, recorded at 16kHz in a PCM 16bit format of words spoken separately).
Since I want to spot the words in a sentence, I'm not sure the model they provide (cnn) is suited for this use case (is it?). I'm thinking about something more like LSTMs. So I have few questions:
- Is there a way to do it "simply" using convnets or something similar?
- Is LSTM the best suited network for this task?
- If yes, is it possible to use Google's dataset of separately spoken words to train a LSTM network?
- If not, what kind of network should I use?
Any answer, even if not complete, would be greatly appreciated! Thanks!
EDIT: I realized that my post could be confusing, but really what I want to know is if google's dataset of separately spoken words is enough to train a network to recognize spoken words within a sentence. And if yes, what kind of network should I use ?