Answers.Body: <p>Look for sequence-to-sequence modelling, aka, seq2seq.
  <a href="https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras.html" rel="nofollow noreferrer">https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras.html</a>
  <a href="https://en.wikipedia.org/wiki/Seq2seq" rel="nofollow noreferrer">https://en.wikipedia.org/wiki/Seq2seq</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-07-25T16:36:04.780Z
Answers.LastActivityDate: 2021-07-25T16:36:04.780Z
Answers.UserDisplayName: meliksahturker
Body: <p>I want to create an AI that converts words to  International Phonetic Alphabet (IPA), but I am not sure which architecture I am supposed to use.</p>
  <p>It is not possible to translate the characters one by one since there are multiple characters in the source word corresponding to one IPA character. There are solutions for this kind of problem, for example using an Encoder that encodes the content of the input which the decoder then translates, but I am uncertain if this isn't too abstract for this problem.</p>
  <p>Can anyone think of a suitable solution for this task?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-07-20T15:45:38.607Z
Comments.Text: You could try a finite state machine.
Comments.UserDisplayName: Oliver Mason
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-07-20T15:50:36.117Z
Comments.Text: I don't think what you're suggesting is too abstract -- this sounds suitable for seq2seq architectures, such as those used in neural machine translation. It might not be the simplest possible model for the job though. You might be able to adapt [this tutorial](https://keras.io/examples/nlp/lstm_seq2seq/).
Comments.UserDisplayName: chang_trenton
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-04-11T05:24:03.900Z
Comments.Text: You may find this useful, what you describe is (I think) the extraction of grapheme from text and mapping of grapheme to phonemes. See https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/tts/intro.html
Comments.UserDisplayName: David Waterworth
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-04-11T05:25:11.680Z
Comments.Text: So as the answer below mentions it'sa  seq2seq problem, i.e. commonly LSTM or perhaps a trasnformer encoder/decoder network.
Comments.UserDisplayName: David Waterworth
Tags: <natural-language-processing>
Title: Building an AI that predicts the pronunciation of words
UserDisplayName: sb27
ThreadText: # Building an AI that predicts the pronunciation of words
  
  (Asked by: username_0 on 2021-07-20)
  
  I want to create an AI that converts words to International Phonetic Alphabet (IPA), but I am not sure which architecture I am supposed to use.
  
  It is not possible to translate the characters one by one since there are multiple characters in the source word corresponding to one IPA character. There are solutions for this kind of problem, for example using an Encoder that encodes the content of the input which the decoder then translates, but I am uncertain if this isn't too abstract for this problem.
  
  Can anyone think of a suitable solution for this task?
  
  **Tags:** natural-language-processing
  > **Comments:**
  > **username_1**: You could try a finite state machine.  
  > **username_2**: I don't think what you're suggesting is too abstract -- this sounds suitable for seq2seq architectures, such as those used in neural machine translation. It might not be the simplest possible model for the job though. You might be able to adapt [this tutorial](https://keras.io/examples/nlp/lstm_seq2seq/).  
  > **username_3**: You may find this useful, what you describe is (I think) the extraction of grapheme from text and mapping of grapheme to phonemes. See https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/tts/intro.html  
  > **username_3**: So as the answer below mentions it'sa  seq2seq problem, i.e. commonly LSTM or perhaps a trasnformer encoder/decoder network.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_4 on 2021-07-25. Score: 0
  
  Look for sequence-to-sequence modelling, aka, seq2seq.
  <https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras.html>
  <https://en.wikipedia.org/wiki/Seq2seq>
  
  
  ---
  
  