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ON THE REPRESENTATION OF VOICE SOURCE APERIODICITIES IN THE MBE SPEECH CODING MODEL Preeti Rao and Pushkar Patwardhan Department of Electrical Engineering,

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Presentation on theme: "ON THE REPRESENTATION OF VOICE SOURCE APERIODICITIES IN THE MBE SPEECH CODING MODEL Preeti Rao and Pushkar Patwardhan Department of Electrical Engineering,"— Presentation transcript:

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2 ON THE REPRESENTATION OF VOICE SOURCE APERIODICITIES IN THE MBE SPEECH CODING MODEL Preeti Rao and Pushkar Patwardhan Department of Electrical Engineering, Indian Institute of Technology, Bombay India

3 Department of Electrical Engineering, IIT Bombay 2 The MBE Speech Model (Griffin & Lim, 1988) X MBE modeling Original Modeled

4 Department of Electrical Engineering, IIT Bombay 3 Frame-based analysis Within the window, assume: a constant–amplitude, constant- frequency sinusoidal model

5 Department of Electrical Engineering, IIT Bombay 4 MBE Speech Model Parameters Pitch Harmonic amplitudes Band-wise voicing decisions Parameter Estimation Windowed speech (Phase is predicted for smoothness)

6 Department of Electrical Engineering, IIT Bombay 5 MBE Analysis: Parameter Estimation Pitch and Spectral Amplitudes : Analysis-by-synthesis matching of a predicted harmonic spectrum with the actual signal spectrum. Voicing decision per frequency band (3 harmonics): Based on the error between the actual and predicted spectra.

7 Department of Electrical Engineering, IIT Bombay 6 MBE Analysis: Spectral Matching Voicing thresholds are frame- adapted as determined by experimental tuning.

8 Department of Electrical Engineering, IIT Bombay 7 MBE Synthesis Voiced amplitudes White noise Unvoiced amplitudes Reconstructed speech Bank of Harmonic Oscillators Pitch Voiced speech Voiced speech synthesis Unvoiced speech Linear Interpolation STFT Replace Envelope Weighted Overlap-Add Unvoiced speech synthesis Voiced speech Unvoiced speech

9 Department of Electrical Engineering, IIT Bombay 8 The efficient quantisation of MBE parameters has led to: IMBE 4.15 kbps DVSI MBE >2 kbps LR MBE 1.5 kbps Research groups: (Univ. Surrey, UCSB, Sony kbps to 3 kbps Narrowband Speech Coding with MBE modeled reference

10 Department of Electrical Engineering, IIT Bombay 9 Related Models: Speech Synthesis Harmonics+Noise Model (HNM): Stylianou Harmonic/Stochastic Model (H/S): Dutoit,1996 Emphasis is on natural sounding wideband speech and easy prosody modification. Both use essentially the Griffin & Lim MBE analysis. Important differences: Analysis and synthesis are pitch synchronous Estimated harmonic phases are utilised in synthesis

11 Department of Electrical Engineering, IIT Bombay 10 MBE Model: Limitations The codec speech quality does not improve with increasing bit rate => the model has its limitations Assumption of frame-level quasi-stationarity: enables the accurate representation only of vowels unvoiced and voiced fricatives (not plosives, onsets,…)

12 Department of Electrical Engineering, IIT Bombay 11 dark sharp Glottal pulse shape variation (brightness, vocal effort) Pitch cycle variations: Jitter / shimmer (roughness / harshness) Frication and aspiration (friction, breathiness) T2Tm T1 + Glottal pulse Vocal tract response Speech signal Steady Sounds: Voice Quality

13 Department of Electrical Engineering, IIT Bombay 12 Role of Model Excitation Parameters The glottal spectral shape (glottal waveform shape) can be captured by the spectral envelope parameters. But the perceptual effects of vocal cord vibration aperiodicities aspiration / frication noise must be reproduced (if at all) by the MB excitation.

14 Department of Electrical Engineering, IIT Bombay 13 Effect of Aperiodicities on MBE Parameters Voice source aperiodicities distort the harmonic spectrum (esp. if the frame contains several pitch cycles). Modulation (jitter-shimmer) aperiodicities => smearing of harmonic lobe structure; noise and subharmonics may be introduced. Aspiration noise => additive noise in harmonic regions

15 Department of Electrical Engineering, IIT Bombay 14 MBE Analysis: Aperiodic Vowel Increase in the analysis spectrum matching error => MBE synthesis of UV (random noise) frequency bands

16 Department of Electrical Engineering, IIT Bombay 15 Previous: On Multi-band Excitation Fujimura, 1968: A crude approximation of aperiodicity observed in natural speech can be made by distributing patches of random noise signals in the time-frequency space of the speech signal. Makhoul, 1978: Spectral devoicing due to vocal cord vibration irregularities is an artifact of the spectral estimation, and it may not be appropriate to use a noise source for the synthesis… Griffin and Lim, 1988: Justify MBE model by quoting Fujimura, and also their own observations with speech in noise.

17 Department of Electrical Engineering, IIT Bombay 16 Synthetic Vowel : Modulation Aperiodicities

18 Department of Electrical Engineering, IIT Bombay 17 Synthetic Vowel : Modulation Aperiodicities HIGH JITTER HIGH SHIMMER 80 Hz 160 Hz 250 Hz Periodic ref:

19 Department of Electrical Engineering, IIT Bombay 18 Fujimura-type Experiment Highly jittered vowel / ɑ / Reference MBE (note unfused noise) MBE-modeled with forced decisions

20 Department of Electrical Engineering, IIT Bombay 19 Experiments with Natural Speech Goal: to study the MBE representation of Unvoiced and voiced fricatives Breathy voice Rough and hoarse voices Speech in noisy background To understand the implications of simplifying the excitation to single-band (SBE) or two-band excitation (TBE)

21 Department of Electrical Engineering, IIT Bombay 20 VCV: /ɑzɑ/ Reference MBE-Modeled SBE modeled

22 Department of Electrical Engineering, IIT Bombay 21 VCV: /ɑƷɑ/ Reference MBE-modeled

23 Department of Electrical Engineering, IIT Bombay 22 Voice quality: Breathy MBE-modeled TBE-modeled (buzzy) Reference

24 Department of Electrical Engineering, IIT Bombay 23 Voice Quality: Harsh MBE-modeled TBE-modeled Reference

25 Department of Electrical Engineering, IIT Bombay 24 Voice Quality: Rough MBE-modeled Reference

26 Department of Electrical Engineering, IIT Bombay 25 Noise Corrupted Speech (15 dB SNR) Reference MBE-modeled TBE-modeled (buzzy)

27 Department of Electrical Engineering, IIT Bombay 26 Conclusions MB excitation represents frication and aspiration accurately; esp. crucial for noisy speech. Modulation aperiodicities are not captured at high pitches except through devoiced bands. Depending on the setting of thresholds, the noise bands may not fuse perceptually. It is possible to simulate partially the perceptual effects of jitter/shimmer by the controlled devoicing of bands in the t-f space.

28 Thank you


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