When I saw the poster for Google Summer of Code in my department, it was already April 6th. Thanks to the time difference, I still had one day to apply before the deadline. Searching the list of projects with "speech recognition" as the keyword showed CMU Sphinx as the only result. It was great that there was something related to my research interests, which include acoustic modeling and speaker adaptation. While checking the CMU Sphinx project page, I was so excited to see the language learning project there. I had published a paper on that topic. That's what I will do! I contacted the mentor, James, for that project. He is really nice and gave me quite a lot of suggestions for my application. Also I have to thank Ronanki, who may not know that his well written project proposal helped me a lot with my application.
Finally, both Ronanki's Pronunciation Evaluation using CMU Sphinx3 and my Accurate and Efficient Pronunciation Evaluation using CMUSphinx for Spoken Language Learning proposals were both accepted this Monday! Thanks so much to all the mentors, reviewers and also to Google for providing us this great opportunity to work on open source projects.
Pronunciation learning is one of the most important parts of second language acquisition. The aim of this project is to utilize automatic speech recognition technology to facilitate learning spoken language and reading skills. Ronanki and I will work on the same pronunciation evaluation project with different focuses. Ronanki will focus on building the web-based pronunciation evaluation system with CMU Sphinx3. I will mainly focus on developing edit-distance based mispronunciation detection grammars, speech data collection, and maximizing the potential learner population by implementing a mobile application to work with our pronunciation evaluation system. Additionally, we also plan to design and implement an game front end to make the learning process much more fun. My project involves four specific sub-tasks: automatic edit distance scoring grammar generation, exemplar pronunciation data collection, an Android app client implementation, and development of a game-based learning system.
As a first time open source contributor, there are lots of things to learn. I believe we will have a great summer this year. Also any comments or suggestions are appreciated. Thanks again for everyone that made this happen!
Finally, both Ronanki's Pronunciation Evaluation using CMU Sphinx3 and my Accurate and Efficient Pronunciation Evaluation using CMUSphinx for Spoken Language Learning proposals were both accepted this Monday! Thanks so much to all the mentors, reviewers and also to Google for providing us this great opportunity to work on open source projects.
Pronunciation learning is one of the most important parts of second language acquisition. The aim of this project is to utilize automatic speech recognition technology to facilitate learning spoken language and reading skills. Ronanki and I will work on the same pronunciation evaluation project with different focuses. Ronanki will focus on building the web-based pronunciation evaluation system with CMU Sphinx3. I will mainly focus on developing edit-distance based mispronunciation detection grammars, speech data collection, and maximizing the potential learner population by implementing a mobile application to work with our pronunciation evaluation system. Additionally, we also plan to design and implement an game front end to make the learning process much more fun. My project involves four specific sub-tasks: automatic edit distance scoring grammar generation, exemplar pronunciation data collection, an Android app client implementation, and development of a game-based learning system.
As a first time open source contributor, there are lots of things to learn. I believe we will have a great summer this year. Also any comments or suggestions are appreciated. Thanks again for everyone that made this happen!
All the posts for GSoC 2012 will also appear in our team blog: http://pronunciationeval.blogspot.com/.
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