In acoustic model,
- each word consists of syllables
- each syllable consists of phonemes
- each phoneme consists of some (hypothetical) states.
Each phoneme can be described by a HMM (acoustic model). Each time frame, with an observance (MFCC vector) mapped to a state. Hence, there are state transition probabilities a_ij and observation distribution b_j(o_t) in each phoneme acoustic model.
Usually in speech recognition we restrict the HMM to be a left-to-right model, and the observation distribution are assumed to be a continuous Gaussian mixture model. Here we use characters to simulate the observance.
- Algorithm: Baum-Welch
- input:
- observed sequences (seq_model_01~05.txt)
- initial state & transition probabilities (model_init.txt)
- number of iteration
- output:
- trained models for 5 sequences
- Algorithm: Viterbi
- input:
- trained model
- testing sequences
- modellist.txt
- output
- best labels (with the higest probability among the five models)
see the document