## CSPP 56553 - Artificial Intelligence Winter 2004 Homework #6: Due March 3, 2004

### Goals

Through this assignment you will:
• Explore Weighted Automata/Markov Models as a mechanism for reasoning with uncertainty over time.
• Experiment with the use of these models for pronunciation modeling and speech recognition.

# Non-Programming Alternative

## Modeling Pronunciation

In lecture we discussed a model of the pronunciation of the word about that had been extracted from the Switchboard corpus, a collection of conversational telephone speech. Here we consider pronunciation from a different data source - TIMIT - a phonetically structured corpus of read speech. For TIMIT, participants were asked to a read back a set of sentence prompts. These sentences were constructed to cause each phoneme to appear in as many contexts as possible. Below, you will see a set of pronunciations for the word "permanent" automatically extracted from close manual phonetic transcriptions of the TIMIT recordings. We will construct a weighted automaton model of this word and use it to perform some calculations.
• pcl p er m ix nx eh n
• pcl p er m ix n ih n tcl t
• pcl p er m n ah n tcl t
• pcl p er m ix n eh q
• pcl p er m ix nx ix q
• pcl p er m n ih n tcl t
• pcl p er m ax nx ix n tcl t
• pcl p er m ah n eh n q

### Part A

Identify the states and the legal transitions between states.

### Part B

Compute the weights (transition probabilities) for each transition in your automaton, based on the small corpus of pronunciations.

### Part C

What is the probability of the pronunciation "pcl p er m ax nx ix n q" according to the model? ('q' represents a glottal stop; it's not a typo)

### Part D

Based on this automaton, what is the most probable pronunciation? What is its probability?

# Programming Alternative

### Problem 1

Implement the Viterbi algorithm.

Apply your implementation to either the "tomato" or "about" automata. Demonstrate the option of the algorithm on two pronunciations.

Note: You only need to return the maximum probability; you do not need to return the path (unless you want the extra challenge).