The Hidden Markov Model is a finite set of states, each of which is associated with a (generally multidimensional) probability distribution. Transitions among the states are governed by a set of probabilities called transition probabilities. In a particular state an outcome or observation can be generated, according to the associated probability distribution. It is only the outcome, not the state visible to an external observer and therefore states are ``hidden'' to the outside; hence the name Hidden Markov Model.

We can use HMM's to perform isolated word recognition

Can use HMMs for continuous speech recognition (not isolated words)

HMMs are based on a sound probabilistic framework and have an integrated framework for simultaneously solving the segmentation and classification problem (the difficulty for a computer in distinguishing speech from silence, in order to segment the speech into words), which makes them suitable for continuous speech recognition. Other systems, where detection of middle silence (a pause of some unintelligible utterance in the middle of speech) is difficult, the user is requested to utter each word separately and wait for the system to recognize, making it difficult for the users to have a "natural" interface to the machine, an interface where the flow of conversation is not interrupted by forced pausing.

By considering speech to be an ordered collection of phonemes, it has become easy to recognize speech independent for the speaker's accent. Training is required, but in independent speech recognition systems, this is done when the model is constructed by using large samples.