1. Speech Recognition Problem and Hidden Markov Model Ziba Rostamian CS 590 - Winter 2008

2. Definition of the Problem Speech recognition is the process of converting an acoustic signal, captured by a microphone or a telephone, to a set of words. Example of speech recognition application : Simple data entry (e.g.,entering a credit card number) speech-to-text processing (e.g.,word processor or e-mail) Smart voice recognition or SYNC’s technology which enable you to talk to your iPod or other portable digital music player.

3. Speech Recognition Parameters ParametersRange Speaking modeIsolated words to continues speech Speaking styleRead speech to spontaneous speech EnrollmentSpeaker dependent to speaker independent. VocabularySmall words to large words

4. State of Art Substantial progress has been made in the basic technology, leading to the lowering of barriers to speaker independence, continuous speech, and large vocabularies. One of the factors that have contributed to this progress is the coming of age of the HMM. HMM is powerful in that, with the availability of training data, the parameters of the model can be trained automatically to give optimal performance advances in computer technology have also indirectly influenced our progress

5. Markov chain & Markov property Consider a set of states, The process starts in one of the states and moves successively from one state to another one (possibly backs to the same state).

6. Markov chain & Markov property Moving from one state to another state is based on the probabilities between states. These probabilities have been called transition probabilities. Properties:

7. Markov chain & Markov property Markov property : Having the Markov property means the next state only depends on the present state, but not on the previous states.

8. Markov chain & Markov property Example : Consider a simple 3- state Markov model of the weather.

9. Example (cont) Given that the weather on day 1 (t = 1) is sunny (state 3) what is the probability that the weather for next 7 days will be “sun-sun-rain-rain-sun-cloudy-sun…”? where

10. Hidden Markov Model Coin Toss Model : There is room with a curtain that divide the room. There is a person on the other side on the curtain who is performing a coin tossing experiment. He doesn’t tell you what he is doing exactly. He will only tell you the result.

11. References A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition.