Due Dates for 2015 still under construction
When submitting assignments
- Zip all files together with your name in the file name
- Include you name in the zip file
- Submit through Latte
Late submission rules
- You can only get an extension by asking for permission before the due date
- No permission will be granted after the due date except for cases of dire emergency
- While I accept all reasonable excuses, don't overuse the privilege
- Waiting until the 11th hour to start an assignment is not a reasonable excuse
Links to Assignments
Assignment 1: Speech application review
Review due September 1st and at least one comment by the 3rd (can continue discussions beyond that, of course)
- Select a speech application and try it out. Be creative--there are a lot of them out there.
- Write a short review (2-3 paragraphs)
- Describe the application (functionality, platform)
- Report how well it works, including what you tried, what worked and what didn't
- Describe overall usefulness and limitations
- Optionally include information about the company or (attributed) quotes from other product reviews
- Post it to the "Speech Recognition Application review"s forum on Latte.
- Read the reviews from other classmates and comment on how it compares to the app your reviewed, whether you've used anything similar, or ask for more information about the app or its performance.
- Throughout the semester if you run across new applications or information about existing apps that you think is interesting, post them to this forum
Assignment 2: Evaluating Speech Recognizers
- Target domain: Ordering a pizza
- Detailed information about the assigment and the data are on Git
- You will receive "dev" and "real" test sets made up from audio and transcription each member of the class will submit
- You will receive text data for language modeling from both previous classes assignments and automatically generated data
- Record 10 sentences.
- Submit audio in 16K and 8K and transcription (.ref file).
- Follow formatting guidelines in Git
- Baseline on your sentences: Recognition in the mashup and scoring using sclite
- Receive “dev test” (5 sents from everyone in the class)
- Receive LM text data
- Submit results on Dev test: Your best grammar run, a baseline LM run with all the sentences
- 3 sets of recognition results on Real Test
- Your best grammar on Mashup
- Your best LM on Mashup
- NOTE: The mashup only lets you give it a set of sentences, so improvements are based on the selection of data, not any fancy LM tricks.
- 2 page discussion comparing the results
KEEP ALL YOUR SCRIPTS IN GITHUB
Assignment 3: Perplexity using CMU and SRI toolkits DUe October 13
- Step 1: Baseline
- Install both the CMU and SRI LM toolkits
- build a baseline language model using a training dataset you ran through the Mashup
- run a baseline perplexity on the the dev test data (save the real test for later)
- Step 2: Improve the model (or at least change it)
- Use at least 3 different techniques to change the perplexity
- examples include changing the backoff/discounting strategies, interpolation, class grammars, additional data, data selection,...
- Keep track of everything you change and what it resulted in, whether it got better or worse.
- Step 3: Write a report describing your experiments and discussing the results