1. Communications & Multimedia Signal Processing Report of Work on Formant Tracking LP Models and Plans on Integration with Harmonic Plus Noise Model Qin Yan Communication & Multimedia Signal Processing Group Dept of Electronic & Computer Engineering, Brunel University 14 Feb, 2005

2. Communications & Multimedia Signal Processing Outline Parallel formant synthesizer vs Cascade formant synthesizer MMSE based Pre-cleaning vs LPSS based Pre-cleaning for formant tracking Plan of integration with Harmonic Noise Model (HNM)

3. Communications & Multimedia Signal Processing System Overview

4. Communications & Multimedia Signal Processing Parallel Formant Synthesiser I Figure - Klatt synthesizer Weakness : zeros(troughs) in the overall response of the synthesizer and hard to tuning and control. Strength : Individual gain M i for each formant F i

5. Communications & Multimedia Signal Processing Parallel Formant Synthesiser II Iterative optimization process is employed to control the magnitudes of formants. Note: M i is different from M oi.. Threshold is |M modoi – M oi | <0.5dB Iterative Optimized Freq Response H mod Original Freq Response H Individual Filter Freq Response H i M modoi M oi MiMi

6. Communications & Multimedia Signal Processing Cascade Formant Synthesizer with Adjusted Formant Magnitudes Weakness : only one gain term M for all formants. Hard to adjust magnitude of individual formants. Strength: Overall response is always an all-pole filter even after modifications. No zeros or troughs. Adjustment of magnitudes of individual formant can only be achieved via modification of the bandwidth --- an iterative optimization is required to obtain the required changes between filter parameters. Eg. Decrease B i Increase M i ; Increase B i Decrease M i.. Global SNRSeg SNRLPSSCasFMTCasFMTA 0-0.25.75.055.79 55.38.17.727.95 Performance of cascade formant synthesizer with adjusted formant magnitude

7. Communications & Multimedia Signal Processing MMSE based Pre-cleaning I Figure - Performance comparison of LPSS and MMSE on car noisy speech. MMSE gives better performance in both segmental and global SNR compared with LPSS. NOTE: In both cases SNR is calculated in FFT domain rather than LP domain.

8. Communications & Multimedia Signal Processing Figure: Average % error of formant tracks of speech in train noise and cleaned speech using spectral subtraction and Kalman filters, the results were averaged over five males. MMSE based Pre-cleaning II MMSE is better in all the formants than LPSS. MMSE+Kalman presents better performance than LPSS+Kalman in lower formants but not in higher formants.

9. Communications & Multimedia Signal Processing Future Work Cleaning of the speech excitation --- Using harmonic and noise model (HNM) to model the speech excitation HNM based clean speech synthesizer. Pitch tracking in noise conditions. Maximum voiced frequency estimation. HNM based speech/excitation enhancement.

10. Communications & Multimedia Signal Processing Thank You!