Hidden Markov Model for Speech Rcognition. HMM Number of States

1. What are hidden states in HMM?
2. How many parameters do we have for this HMM?
3. How are HMMs used in speech recognition?
4. Is HMM a state space model?

What are hidden states in HMM?

Hidden Markov model is basically a Markov chain whose internal state cannot be observed directly but only through some probabilistic function. That is, the internal state of the model only determines the probability distribution of the observed variables.

How many parameters do we have for this HMM?

Any HMM can be defined with five parameters i.e., (N,M,A,B,andπ) where N is the number of hidden states.

How are HMMs used in speech recognition?

The main core of HMM-based speech recognition systems is Viterbi algorithm. Viterbi algorithm uses dynamic programming to find out the best alignment between the input speech and a given speech model.

Is HMM a state space model?

State space models (SSM, also known as hidden Markov models, HMM) are latent variable models which are commonly applied in analysing time series data due to their flexible and general framework.