Post-grad speech, audio processing & recognition coursework - Hidden Markov Model invesigations. Achieved 98%
|
|
2021-01-01 18:53:11 +00:00 |
|
.vscode
|
report outline, added state transitions to constants
|
2020-12-10 22:59:55 +00:00 |
|
report
|
gathered resources, working on report
|
2021-01-01 18:53:11 +00:00 |
|
.gitignore
|
finished code, beginning writeup
|
2020-12-31 19:30:39 +00:00 |
|
constants.py
|
beginning markov code
|
2020-12-11 21:33:20 +00:00 |
|
markov.ipynb
|
gathered resources, working on report
|
2021-01-01 18:53:11 +00:00 |
|
markov.py
|
gathered resources, working on report
|
2021-01-01 18:53:11 +00:00 |
|
markovlog.py
|
finished code, beginning writeup
|
2020-12-31 19:30:39 +00:00 |
|
maths.py
|
beginning markov code
|
2020-12-11 21:33:20 +00:00 |
|
notebook.py
|
gathered resources, working on report
|
2021-01-01 18:53:11 +00:00 |
|
README.md
|
gathered resources, working on report
|
2021-01-01 18:53:11 +00:00 |
|
requirements.txt
|
report outline, added state transitions to constants
|
2020-12-10 22:59:55 +00:00 |
|
scratchpad.ipynb
|
gathered resources, working on report
|
2021-01-01 18:53:11 +00:00 |
Hidden Markov Models
Speech recognition coursework focusing on training and analysing hidden markov models.
Probability density functions with provided observations marked
Occupation likelihoods for each state through time
Output Gaussian functions through 5 iterations of Baum-Welch training
