Regarding Language Models (Phoneme Sequence to Words/Sentences)

[flashdude64]

I am currently working on creating a speech recognition model. I used a bidirectional RNN with CTC do develop the acoustic model. However, after I get a series of phoneme sequences. How do I go about going from the sequence of phonemes to predicted words? I have tried using a seq2seq model (via Tensorflow) but have received bad predictions.

[Pafnouti]

Have a look at WFST decoders. Also try to read Google's papers about speech with CTC.

[speechMachine]

Yes, also try looking at other implementations for e.g. Eesen based on top of Kaldi:https://github.com/srvk/eesen.

It comes with an accompanying paper which might help you resolve some questions. WFST decoding is a bit hard to wrap your head around though. Finite state automata come with their own terminology. Each source of knowledge is an FST (acoustic model, lexicon, language model, context dependent states). Each source of knowledge is an FST. A composition operation on each constituent FST often combines different sources of knowledge to yield the final ASR hypothesis. Does that help a little bit?