What are the steps for training an acoustic model? The format of the data (the audio) includes its length and other characteristics. If anyone could provide a simple example of how to train an acoustic model, it would be greatly appreciated.
This paper has details on how to prepare Audio data (and merge it with Language Models) for speech-to-text :
Slide 16 has very high level description of feature engineering.
Following papers provide more detailed analysis of audio processing for speech to text :
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.701.6802&rep=rep1&type=pdf http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.689.4627&rep=rep1&type=pdf https://www.ijariit.com/manuscripts/v4i3/V4I3-1646.pdf
There are many different ways to do this. It depends a lot on what you want to do. A general dictation algorithm which works for most people who speak the same language? One that works or optimises itself for each person? Just a few keywords to input into a device? And so on.
I'd personally just download some of the open source versions on the internet (https://github.com/topics/speech-to-text), try to understand how they work (literature like the one above can help), and then write something that fits my requirements. That will also help avoid unnecessary complications when trying to interpret sounds. Translating sounds into phonemes, for instance, is a step which for most applications will only add complexity and, with the differences in how people pronounce things, increase errors. A database/table with the sounds of complete syllables or words is more likely to lead to results in many use cases. The database should be optimised each time a user corrects something the computer wrote - by adding the according sounds-word combination, by increasing the priority of the solution which turned out to be correct if there where other possible choices, or by using surrounding words as indicators of the correct choice (dynamic word-pairing).
Be aware that even if your code identifies sounds better than any human, it will still make a lot of mistakes with similar sounding words, or by missing where one word ends and the other begins. The usual way around is word pairs - if you have different possibilities, you choose the ones where words often come together. That may even produce text better than most humans - but still with very funny misinterpretations. If you want to avoid that, I'm afraid you'd have to model a far more complex ai which does not yet exist except maybe in a few labs - making sense of what someone says so it can have informed guesses which of many similar sounding options were actually meant. Not to mention catching any errors by the speaker.
If you only need a very limited vocabulary, you could also let people train the device (which would then work for any language) and maybe learn from experience - like when a word is pronounced differently sometimes. This can be done as easily as just recording a sound for each word. Adding similar sounds to each word when the user doesn't quickly change his mind (an indication that the computer misunderstood) or different sounds when a correction occurs is also an option, and helps improve recognition. The sounds and words could also be averaged out or parsed for differentiating parts, for better hit rates. Such measures would also work for above database.