Handling Multiple Input Types
Multiple input types are common as learning technology moves from academic labs and open source examples into the real world. The design process includes the below four steps, which related directly to this question. There are other steps that don't centrally related not mentioned here.
First Step — List Inputs in Most ...
Yes, intuition says that RNNs like LSTM or GRU will work better in your case, because predicted values might depend on input patterns corresponding to much earlier time intervals.
There is no reason to create samples shifted by a single measurement because many of the samples will contain pretty much the same information for your model. A viable approach is ...
Google has an API you can use. https://cloud.google.com/translate/. Their API can translate audio to text. They also have an API for converting speech to text. The language detection feature should let you detect the language in the resulting text. They have client libraries for the most popular programming languages.
The task of isolating 2 or more speakers is called speaker diarization,
here a list of softwares and useful resources.
Once you have the 2 or more audio files containing the individual voices, you could run some speech-to-text network that also outputs time information.
OK, here is one approach.
Acquire a data set of 'clean' audio samples without barking dogs and an data set of barking dogs sounds.
Generate a training set by mixing random selections of clean audio with a random selections of barking dog noises at various volume levels. This is your input data. Your output data is the clean audio before you added the ...
Since you require a voice-operated system, the first thing you will
need is a speech-to-text converter. A good speech-to-text engine
would be able to recognize words properly and thereby increasing the
user-friendliness. A normal person could not create or train it.
These engines are provided by Google Cloud.
Next thing, is an intelligent system which would ...