From SpeechWiki

The goal of these experiments is to explore the utility of using mixed units (phones, syllables and whole words) for large vocabulary speech recognition. These experiments use the Fisher Corpus for training.

Baseline

First we need a reasonable traditional phone-based recognizer against which to compare. An implementation of it using GMTK and associated scripts is here.

It uses the phone set, the multi-pronunciation phonetic dictionary and MVA-normalized PLP observations described in Fisher Corpus.

Monophone Model

The initial model is monophone, with the number of states per phone specified in phone.state. The number of gaussians per state will be determined by tuning.

Training on the entire fisher training set (1775831 utterances, specified in Fisher Corpus), takes an exceedingly long time: ~15 hours for a single EM iteration, on a 32 cpu cluster. Possible solutions are:

• Do training on data where word boundaries are observed. (Thanks Chris) For this we need to force-align the entire fisher corpus using some existing recognizer.
• Use a smaller corpus.
• Use teragrid (our allocation of 30000 cpu-hours is only 40 days on our cluster - almost not worth the effort of porting)
• Fool around with better triangulations (not much hope for improvement since the RVs with each frame are densely connected in our DBN)

Decoding

Uses the 20k vocabulary language model described in Fisher Corpus.