Download EEL6586: Automatic Speech Processing - HW#4 Assignment - Prof. John Gregory Harris and more Assignments Electrical and Electronics Engineering in PDF only on Docsity!
EEL 6586: HW#
Assignment is due Friday, February 29, 2008 in class. Late home- work loses e#^ of^ days late^ − 1 percentage points. This assignment in- cludes both matlab and textbook questions.
This assignment must be completed in teams. Only one assignment should be turned in with all the names of the team members written on the front. The teams have been arbitrarily assigned as follows:
- Arvind Iyer and Yan Yang
- Kiran Kalidindi and Yu Fan
- Salil Bibikar and Steven Spalding
- Avantika Vardhan and Steven Wright
- Karthik Talloju and Jiang Lu
- Surbhi Singhal and Justin Zito
- Padmanabhan Ramakrishnan and Yuelu Liu
- Rahul Chaithanya and Oluwatosin Adeladan
- Reno Varghese and Sarah Keen
- Siddharth Gaddam and Ming Xue
- Keshav Rajan, Duroseme Taylor and Eric Graves
Teammates should work together on each of the problems and agree on their solutions. As in real life, dysfunctional teams will be severely penalized.
PART A: Thought Problems
A1 All other factors being equal, do you expect speaker-independent ASR systems to have a lower error rate than speaker-dependent ASR sys- tems? Explain.
A2 Briefly explain how linguistic constraints can improve a speech recog- nition system.
A3 Class ω 1 points are:
Class ω 2 points are:
Find any weight vector w such that wT^ x > 0 for all class ω 1 points and wT^ x < 0 for all class ω 2 points. Justify your answer.
A4 Using the points in [A3] as the training set, classify [1, 1 , 1]T^ using 3- Nearest Neighbor voting. Remember that in k-NN classification, the k nearest neighbors of the point are found and the most common class label is used for classification.
A5 Consider the following sample points:
The samples from class 1 are:
[ 0 0
] [ 1 1
] [ 2 1
]
The samples from class 2 are:
[ 1 0
] [ 1 − 1
] [ 1 − 1
] [ 1 − 2
]
Sketch the 1-Nearest Neighbor class boundaries for this set of sample points. Clearly label the class on either side of the boundary.
B2 Use a classification algorithm to classify the test data. Again you are free to use any classifier you like (Nearest Neighbor, Bayes, Neural Network, HMM, ...) Briefly explain why your choice of a classifier is a wise one (even if you decide to stay with the nearest neighbor classifier).
B3 Always include several trials as in hw4Demo.m and report the average over all trials. For your final version, make sure to include at least 100 trials. What is your final accuracy rate? What is the standard deviation of your accuracy value?
B4 For your final optimized system, which two phonemes are most likely to be confused with one another?
B5 Comment on why it is important that no speaker appear in both the test/training datasets.
As usual, attach all of your code to the end of the assignment. A total of 5 Bonus points will be awarded to the person(s) with the highest percentage correct classification.
