New answers tagged natural-language-processing
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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.
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You would need to perform some kind of speech-to-text to get the audio transcription with the corresponding synchronization wrt the audio. Then search in the transcription.
You could use DSAlign by mozilla
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It depends on the data. If it is structured like form data, then you might not need AI at all — simple regular expression patterns might be fine. This would apply for example to address data. If you have the word street followed by a colon, followed by some text, it seems fairly obvious that this is the name of a street, and possibly also a house ...
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But because the inputs have to have a fixed length
Do they? Why? The go-to strategy would be to use an RNN (possibly with LSTM or GRUs, but probably not necessary) and train it to process input sequentially and output the final classification of the paragraph. This has the advantage of being able to take into account word order and constellations, as ...
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Summing up a sequence of word vector maybe used in practice sometimes. However, the operation of addition is non-reversible, meaning that once you sum up a few numbers, you cannot get the original numbers back. However summing up a sequence of word vectors may work depending on your task. You should also normalize the values, or just use average value.
For ...
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First of all, there are multiple factors on how well models will work. Amount of data, source of data, hyperparameters, model type, training time etc... All of these will affect the accuracy. However, no classifier will work best in general. It all depends on the different factors, and not one can satisfy all, at least for now.
For improving the accuracy, ...
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The accuracy depends on various factors. Might not always be the algorithm. For example a cleaner data with a poor algorithm might still give better results and vice versa.
What are the preprocessing techniques you are using? This preprocessing techniques article is a good starting point for html data. And by vectorising I assume you mean word2vec, use a ...
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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.
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