1. Splitting Phonemes Adam Burkhalter Duong Chau Micah Lee

2. Project Overview ●Isolate phonemes in a signal o Read in an audio file o Separate the individual phonemes o Determine phoneme signature o Output results

3. Audio Reader Application ●Audio Recorder using Java o Save audio as.WAV o Save audio as bytes ●Playback audio ●Add saved audio

4. Key Algorithm Vocabulary ●Phonemes ●Exponential Sinusoidal Model ●Accumulated Autocorrelated Functions ●Transients ●Variable Segmentation Strategy

5. Phonemes Is a basic unit of a language’s phonology, which is combined with other phonemes to form meaningful units such as words or morphemes. Cow has 2 phonemes: \c and \ou

6. Example of Phonemes /A/a (table), a_e (bake), ai (train), ay (say) /a/a (flat) /b/b (ball) /k/c (cake), k (key), ck (back) /d/d (door) /E/e (me), ee (feet), ea (leap), y (baby)

7. Transients ●High amplitude, short-duration sound ●At beginning of a waveform ●Occurs in phenomena such as musical sounds, noises or speech

8. Phoneme Boundaries ●Transients model phoneme boundaries

9. Exponential Sinusoidal Model (ESM) ●Amplifies signal content o Extreme values get amplified o Low values do not ●Helps determine phoneme boundaries

10. Accumulated Autocorrelated Functions (AACF) ●Determines the coefficients of the ESM

11. Variable Segmentation Strategy ●Uses high difference transients to determine phoneme boundaries ●Boundaries are used to isolate phonemes for processing

12. Phoneme Processing ●FFT analysis to determine signal composition ●FFT data used to create filter banks ●Filter banks used to identify phonemes ●Identified phonemes are characterized ●Results displayed

13. References Parameterizing Speech Phonemes by Exponential Sinusoidal Model -Jayanth Kumar Talasila and Pradip Sircar