2. Korean Phoneme Discrimination Ben Lickly

3. Motivation Certain Korean phonemes are very difficult for English speakers to distinguish, such as ㅅ and ㅆ. ㅅ (IPA: s) ㅆ (IPA: s )

4. Network Inputs Sound files were edited down to single relevant phoneme. Mel Frequency Cepstral Coefficients (MFCC) were taken over each half of the phoneme. 13 coefficients per half = 26 total inputs

5. Network Outputs Only two outputs, one corresponding to ㅅ and one corresponding to ㅆ. Input sound of ㅅ → network outputs [1,0] Input sound of ㅆ → network outputs [0,1]

6. Network Structure

7. Training data Sample data was gathered from two native speakers. One male speaker, one female speaker Korean audio was also downloaded from internet and edited. A total of 56 samples were collected: 26 samples of each consonant.

8. Training Method Withheld 10% of data for validation Performed 10,000 epochs of backpropagation Often the network did not converge within time frame, or did not classify test data correctly. In these cases multiple rounds of training were needed.

9. Results For network with 5 hidden layer nodes, network took approximately 3 hours to converge. For network with 2 hidden layer nodes, network took approximately 2.5 hours to converge. For network with a single hidden layer node, network was not able to converge in a reasonable time frame (12 hours).

10. Potential Improvements Network does not deal well with sounds that fall between two extremes. One solution would be to add more diversity to sample data. (Include non- native speakers.)

11. Demo/Questions Samples of my speech: ㅅ : ㅆ :