Voice DSP Processing I

1. VoiceDSPProcessingI Yaakov J. Stein Chief ScientistRAD Data Communications

2. Voice DSP Part 1 Speech biology and what we can learn from it Part 2 Speech DSP (AGC, VAD, features, echo cancellation) Part 3 Speech compression techiques Part 4 Speech Recognition

3. Voice DSP - Part 1a Speech production mechanisms • Biology of the vocal tract • Pitch and formants • Sonograms • The basic LPC model • The cepstrum • LPC cepstrum • Line spectral pairs

4. Voice DSP - Part 1b Speech perception mechanisms • Biology of the ear • Psychophysical phenomena • Weber’s law • Fechner’s law • Changes • Masking

5. Voice DSP - Part 1c Speech quality measurement • Subjective measurement • MOS and its variants • Objective measurement • PSQM, PESQ

6. Voice DSP - Part 2a Basic speech processing • Simplest processing • AGC • Simplistic VAD • More complex processing • pitch tracking • formant tracking • U/V decision • computing LPC and other features

7. Voice DSP - Part 2b Echo Cancellation • Sources of echo (acoustic vs. line echo) • Echo suppression and cancellation • Adaptive noise cancellation • The LMS algorithm • Other adaptive algorithms • The standard LEC

8. Voice DSP - Part 3 Speech compression techniques • PCM • ADPCM • SBC • VQ • ABS-CELP • MBE • MELP • STC • Waveform Interpolation

9. Voice DSP - Part 4 Speech Recognition tasks ASR Engine Phonetic labeling DTW HMM State-of-the-Art

10. Voice DSP - Part 1a Speech production mechanisms

11. Esophagus Speech Production Organs Brain Hard Palate Nasal cavity Velum Teeth Lips Uvula Mouth cavity Pharynx Tongue Larynx Trachea Lungs

12. Speech Production Organs - cont. • Air from lungs is exhaled into trachea (windpipe) • Vocal chords (folds) in larynx can produce periodic pulses of air by opening and closing (glottis) • Throat (pharynx), mouth, tongue and nasal cavity modify air flow • Teeth and lips can introduce turbulence • Epiglottis separates esophagus (food pipe) from trachea

13. Voiced vs. Unvoiced Speech • When vocal cords are held open air flows unimpeded • When laryngeal muscles stretch them glottal flow is in bursts • When glottal flow is periodic called voiced speech • Basic interval/frequency called the pitch • Pitch period usually between 2.5 and 20 milliseconds Pitch frequency between 50 and 400 Hz You can feel the vibration of the larynx • Vowels are always voiced (unless whispered) • Consonants come in voiced/unvoiced pairs for example : B/P K/G D/T V/F J/CH TH/th W/WH Z/S ZH/SH

14. Excitation spectra • Voiced speech Pulse train is not sinusoidal - harmonic rich • Unvoiced speech Common assumption : white noise f f

15. Effect of vocal tract • Mouth and nasal cavities have resonances • Resonant frequencies depend on geometry

16. F1 F2 F3 F4 voiced speech unvoiced speech Effect of vocal tract - cont. • Sound energy at these resonant frequencies is amplified • Frequencies of peak amplification are called formants frequency response frequency F0

17. Formant frequencies • Peterson - Barney data (note the “vowel triangle”)

18. Sonograms

19. Cylinder model(s) Rough model of throat and mouth cavity With nasal cavity Voice Excitation open open Voice Excitation open/closed

20. Phonemes • The smallest acoustic unit that can change meaning • Different languages have different phoneme sets • Types: (notations: phonetic, CVC, ARPABET) • Vowels • front (heed, hid, head, hat) • mid (hot, heard, hut, thought) • back (boot, book, boat) • dipthongs (buy, boy, down, date) • Semivowels • liquids (w, l) • glides (r, y)

21. Phonemes - cont. • Consonants • nasals (murmurs) (n, m, ng) • stops (plosives) • voiced (b,d,g) • unvoiced (p, t, k) • fricatives • voiced (v, that, z, zh) • unvoiced (f, think, s, sh) • affricatives (j, ch) • whispers (h, what) • gutturals ( ח,ע) • clicks, etc. etc. etc.

22. Basic LPC Model Pulse Generator U/V Switch LPC synthesis filter White Noise Generator

23. Basic LPC Model - cont. • Pulse generator produces a harmonic rich periodic impulse train (with pitch period and gain) • White noise generator produces a random signal (with gain) • U/V switch chooses between voiced and unvoiced speech • LPC filter amplifies formant frequencies (all-pole or AR IIR filter) • The output will resemble true speech to within residual error

24. Cepstrum Another way of thinking about the LPC model Speech spectrum is the obtained from multiplication Spectrum of (pitch) pulse train times Vocal tract (formant) frequency response So log of this spectrum is obtained from addition Log spectrum of pitch train plus Log of vocal tract frequency response Consider this log spectrum to be the spectrum of some new signal called the cepstrum The cepstrum is the sum of two components: excitation plus vocal tract

25. Cepstrum - cont. Cepstral processing has its own language • Cepstrum (note that this is really a signal in the time domain) • Quefrency (its units are seconds) • Liftering (filtering) • Alanysis • Saphe Several variants: • complex cepstrum • power cesptrum • LPC cepstrum

26. Do we know enough? Standard speech model (LPC) (used by most speech processing/compression/recognition systems) is a model of speech production Unfortunately, speech production and speech perception systems are not matched So next we’ll look at the biology of the hearing (auditory) system and some psychophysics (perception)

27. Voice DSP - Part 1b Speech Hearing &perception mechanisms

28. Hearing Organs

29. Hearing Organs - cont. • Sound waves impinge on outer ear enter auditory canal • Amplified waves cause eardrum to vibrate • Eardrum separates outer ear from middle ear • The Eustachian tube equalizes air pressure of middle ear • Ossicles (hammer, anvil, stirrup) amplify vibrations • Oval window separates middle ear from inner ear • Stirrup excites oval window which excites liquid in the cochlea • The cochlea is curled up like a snail • The basilar membrane runs along middle of cochlea • The organ of Corti transduces vibrations to electric pulses • Pulses are carried by the auditory nerve to the brain

30. Function of Cochlea • Cochlea has 2 1/2 to 3 turns were it straightened out it would be 3 cm in length • The basilar membrane runs down the center of the cochlea as does the organ of Corti • 15,000 cilia (hairs) contact the vibrating basilar membrane and release neurotransmitter stimulating 30,000 auditory neurons • Cochlea is wide (1/2 cm) near oval window and tapers towards apex • is stiff near oval window and flexible near apex • Hence high frequencies cause section near oval window to vibrate low frequencies cause section near apex to vibrate • Overlapping bank of filter frequency decomposition

31. Psychophysics - Weber’s law Ernst Weber Professor of physiology at Leipzig in the early 1800s Just Noticeable Difference : minimal stimulus change that can be detected by senses Discovery: D I = K I Example Tactile sense: place coins in each hand subject could discriminate between with 10 coins and 11, but not 20/21, but could 20/22! Similarlyvisionlengths of lines, tastesaltiness, soundfrequency

32. Weber’s law - cont. This makes a lot of sense Bill Gates

33. Psychophysics - Fechner’s law Weber’s law is not a truepsychophysicallaw it relates stimulus threshold to stimulus (both physical entities) not internal representation (feelings) to physical entity Gustav Theodor Fechner student of Webermedicine, physics philosophy Simplest assumption: JND is single internal unit Using Weber’s law we find: Y = A log I + B Fechner Day (October 22 1850)

34. Fechner’s law - cont. Log is very compressive Fechner’s law explains the fantastic ranges of our senses Sight:single photon - direct sunlight 1015 Hearing: eardrum move 1 H atom - jet plane 1012 Beldefined to be log10 of power ratio decibel (dB)one tenth of a Bel d(dB) = 10 log10 P 1 / P 2

35. Fechner’s law - sound amplitudes Companding adaptation of logarithm to positive/negative signals m-lawandA-laware piecewise linear approximations Equivalent to linear sampling at 12-14 bits (8 bit linear sampling is significantly more noisy)

36. 12 2 Fechner’s law - sound frequencies octaves,well tempered scale Critical bands Frequency warping Melody 1 KHz = 1000, JND afterwards M ~ 1000 log2 ( 1 + fKHz ) Barkhausen can be simultaneously heard B ~ 25 + 75 ( 1 + 1.4 f2KHz )0.69 excite different basilar membrane regions f

37. Inverse E Filter Psychophysics - changes Our senses respond to changes

38. Psychophysics - masking Masking: strong tones block weaker ones at nearby frequencies narrowband noise blocks tones (up to critical band) f

39. Voice DSP - Part 1c Speech Quality Measurement

40. Why does it sound the way it sounds? PSTN • BW=0.2-3.8 KHz, SNR>30 dB • PCM, ADPCM (BER 10-3) • five nines reliability • line echo cancellation Voice over packet network • speech compression • delay, delay variation, jitter • packet loss/corruption/priority • echo cancellation

41. Subjective Voice Quality Old Measures • 5/9 • DRT • DAM The modern scale • MOS • DMOS meet neat seat feet Pete beat heat

42. MOS according to ITU P.800 Subjective Determination of Transmission Quality Annex B: Absolute Category Rating (ACR) Listening Quality Listening Effort 5 excellent relaxed 4 good attention needed 3 fair moderate effort 2 poor considerable effort 1 bad no meaning with feasible effort

43. MOS according to ITU (cont) Annex D Degradation Category Rating (DCR) Annex E Comparison Category Rating (CCR) • ACR not good at high quality speech DCR CCR 5 inaudible 4 not annoying 3 slightly annoying much better 2 annoying better 1 very annoying slightly better 0 the same -1 slightly worse -2 worse -3 much worse

44. Some MOS numbers Effect of Speech Compression: (from ITU-T Study Group 15) • Quiet room 48 KHz 16 bit linear sampling 5.0 • PCM (A-law/mlaw) 64 Kb/s 4.1 • G.723.1 @ 6.3 Kb/s 3.9 • G.729 @ 8 Kb/s 3.9 • ADPCM G.726 32 Kb/s 3.8 toll quality • GSM @ 13Kb/s 3.6 • VSELP IS54 @ 8Kb/s 3.4

45. The Problem(s) with MOS Accurate MOS tests are the only reliable benchmark BUT • MOS tests are off-line • MOS tests are slow • MOS tests are expensive • Different labs give consistently different results • Most MOS tests only check one aspect of system

46. The Problem(s) with SNR Naive question: Isn’t CCR the same as SNR? SNR does not correlate well with subjective criteria Squared difference is not an accurate comparator • Gain • Delay • Phase • Nonlinear processing

47. Speech distance measures Many objective measures have been proposed: • Segmental SNR • Itakura Saito distance • Euclidean distance in Cepstrum space • Bark spectral distortion • Coherence Function None correlate well with MOS ITU target - find a quality-measure that does correlate well

48. Some objective methods Perceptual Speech Quality Measurement (PSQM) ITU-T P.861 Perceptual Analysis Measurement System (PAMS) BT proprietary technique Perceptual Evaluation of Speech Quality (PESQ) ITU-T P.862 Objective Measurement of Perceived Audio Quality (PAQM) ITU-R BS.1387

49. channel QM to MOS QM MOS estimate Objective Quality Strategy speech

50. PSQM philosophy(from P.861) Internal Representation Perceptual model Audible Difference Cognitive Model Perceptual model Internal Representation