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1 ENEE408G Lecture-6 Digital Speech rocessing URL: Slides included here are based on Spring 005 offering in the order of introduction, image, video, speech, and audio. Copyrighted ENEE408G course was ECE Department, University of Maryland, College ar. Inquiries can be addressed to rofs. Ray Liu (jrliu@isr.umd.edu) and Min Wu (minwu@eng.umd.edu). UMC ENEE408G Slides (created by M.Wu & R.Liu 00) Last Lecture Video content analysis Video capturing and display Different video coding standards Today: Go bac to 1-D signal => speech processing UMC ENEE408G Capstone -- Multimedia Signal rocessing UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [] Overview of Speech rocessing Example on Sound Source Feature Aspects of speech linguistic structure Sentence Word Syllable honeme Vowel and consonant Speech production Sound source: voiced ( bu ) vs. unvoiced ( hiss ) sound Articulation UMC ENEE408G Slides (created by Carol Espy-Wilson 004) +voiced // -voiced /s/ UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [3] UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [4] 1

2 UMC ENEE408G Slides (created by M.Wu & R.Liu 00) A Signal rocessing View on Speech roduction UMC ENEE408G Slides (created by Wu/Liu/Espy-Wilson ) Visualie Speech via Spectrogram Short-time Fourier Transform (STFT) of the windowed speech waveform is X ( ω, τ ) = x[ n, τ ]exp[ jω n] A window function is applied to the speech signal x[ n, τ ] = w[ n, τ ] x[ n] Spectrogram is S( ω, τ) = X( ω, τ) Visualied using different brightness in a -D time-frequency plot Frequency Amp [see also SM-survey Fig.3] Time (sec) UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [6] UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [7] UMC ENEE408G Slides (created by Carol Espy-Wilson 004) Frequency (H) Sprouted grains and seeds are used in salads and dishes such as chop suey Sprouted Frequency (H) Time (sec) fricative stop consonant glide vowel stop vowel consonant UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [10] F UMC ENEE408G Slides (created by Carol Espy-Wilson 004) honetic Features (Chomsy & Halle,, 1968) There are three inds of phonetic features Source features determine the ind of excitation signal Manner of articulation features determine how open or closed is the vocal tract lace of articulation features determine the location of primary constriction => Jointly using linguistic nowledge and signal processing approach to identify features UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [11]

3 UMC ENEE408G Slides (created by M.Wu 003) Source-Filter Theory First speaing machine in 1930s NY World s Fair 14 eys, 1 wristband, 1 pedal Modeling speech production as a linear system Sound sources Either voiced or unvoiced Voice sound Modeled by a generator of pulses Unvoiced sound Modeled by white noise generator Articulation Modeled by a cascade of singleresonance (pole) digital filters Figure 1 of SM May 98 Speech Survey Linear Separable Model for Speech roduction Vocal tract is modeled as a linear time-varying system arameters of the linear system are slowly varying Excited by time-varying source (voiced or unvoiced) ractical models Model each speech frame as Linear Time-Invariant Excited by either voiced or unvoiced source Allow overlaps in neighbouring frames Figure 3. of Furui s boo UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [1] UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [13] itch itch period: time duration of one glottal cycle (of glottis) itch: reciprocal of pitch period Also nown as the fundamental frequency Vowel typically has one to four pitch periods itch ranges from about 60H to 400 H Typical pitch range of female pitch is higher than male pitch itch of Male and Female Speaers Distribution, mean, and standard deviation of male and female pitch Figure.1 &.11 of Furui s boo UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [17] UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [18] 3

4 UMC ENEE408G Slides (created by M.Wu & R.Liu 00) Formant Formants: the resonance frequencies of the vocal tract Vowels typically have three formants: F1, F, F3 Related to what has been said and who said it Formant changes when vocal tract changes to produce different sound Figure.5 & 3.5 of Furui s boo Formants of Vowels Figure.6 &.7 of Furui s boo UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [19] UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [1] Speech Coding UMC ENEE408G Slides (created by Carol Espy-Wilson 004) Digital Coding of Speech 00 broadcast quality waveform coding toll quality commun. quality 4.8 Model-based coding Synthetic quality 0.05 bps Waveform coders: quantie speech samples directly at high bit rates. Source coders (vocoders): use nowledge of speech production to parameterie the signal => model based Hybrid coders: partly waveform and partly model based (.4-16 bps) UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [] UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [3] 4

5 Recall: CM Coding for Coding Signal Waveform Sampling Quantiation UMC ENEE408G Slides (created by Carol Espy-Wilson 004) Quantiation Error by A Uniform Quantier parameters: step sie, # of bits B X ea-to-pea range is X, = Assume en [ ] = xn [ ] xn ˆ[ ] where e[n] is uncorrelated with x[n], and it is uniformly distributed σ SNR = = σ 3 1 B x e [ X σx] p e [e] B X σ ( ) = log[ ] SNR db B σ e = 1 x UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [4] UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [5] UMC ENEE408G Slides (created by Carol Espy-Wilson 004) Uniform Quantiation Digital Speech rocessing by Rabiner and Shafer (a) Speech waveform; (b) Error from 3-bit quantiation in same scale as the signal; (c) Error for 8-bit quantiation in a scale magnified 66 times UMC ENEE408G Slides (created by M.Wu & R.Liu 00) Nonuniform Quantiation by Companding Uniform quantiation may give inconsistent range of relative amount errors E.g., +/ incurs 0% vs. % at amplitude 10 and 100 Non-uniform quantiation: e.g. apply log before quanti. Assign smaller quantiation step sie at small amplitude to maintain consistent range of relative quantiation errors over the entire dynamic range Can apply non-linear transform before uniform quantiation via companding (compression-expansion) µ-law companding: international standard for 64bps speech UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [6] UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [8] 5

6 UMC ENEE408G Slides (created by Carol Espy-Wilson 004) Log-based Companding (cont d) Digital Speech rocessing by Rabiner and Shafer But, ln[0] =, not practical xn [ ] log[1 + µ ] X yn = X signxn log[1 + µ ] [ ] ( [ ]) UMC ENEE408G Slides (created by M.Wu & R.Liu 00) Discussion on Improving CM Companding: mae relative quantiation errors more consistent Bring probability into the picture Entropy coding Quantied CM values may not be equally liely Use probability distribution to reduce average # bits per sample Exploit correlation between samples Differential and redictive coding Vector quantiation Exploit different characteristics in different freq. Transform coding and Subband coding (using time-frequency transform) UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [30] UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [3] Recall: redictive Coding Figure 1.1 of Quatieri s boo Linear rediction Analysis of Speech { a i } are called Linear rediction Coefficients (LC coeff.) + s[ n ] + e[n] _ a 1 a a Analysis Error Minimiation Normal equations e [n] + + s[ n] _ a 1 a a Synthesis min E = E( e [ n]) = E( s[ n] sˆ [ n]) { a } n n Saˆ = s Can be solved using the famous Levinson-Durbin Recursion, which leads to lattice formulation of the linear prediction solution UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [33] UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [34] 6

7 All-ole Modeling of Speech Auto-regressive (AR) model: all-pole filter 1 1 H( ) = AG( ) V( ) R( ) = = A( ) 1 a H() is the overall transfer function Glottal Flow G(), Vocal Tract V(), Radiation R() Synthesis process: = 1 u[n]: the vocal tract input, s[n]: speech output 1 Au [n] H ( ) = s[n] A( ) UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [35] All-ole Model and Linear rediction S( ) β β = = U( ) A( ) 1 a = 1 = 1 Here sˆ[ n] = a s[ u ] is a linear prediction of order for s[n] where = 1 _ s [ n ] ( ) + e [ n ] sˆ[ n ] + en [ ] = sn [ ] sn ˆ[ ] S( ) a S( ) = βu( ) = 1 s[ n] = a s[ n ] + β u[ n] sˆ [ n] + e[ n] is the prediction error sequence UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [37] Model-based Coding Line Spectrum air (LS) Coding Linear rediction Coder (LC coder) LC Vocoder ( voice coder ) Divide speech into frames (several tens milliseconds) and encode the LC coefficients of each frame Additional parameters to facilitate synthesis: voiced/unvoiced flag, gain, pitch (for voiced) Line Spectrum air (LS) Coding Hybrid Coding: LC Residual Coding Between LC and waveform coding ros and Cons of LC method Good performance at coding rate down to.4bps Synthesied voice becomes unnatural when below.4bps When the poles are near the unit circle, quantiation in LC coefficients may result in instability. LS parameters LS are frequencies extracted from polynomials constructed from LC coefficients Frequency domain features (similar to formant) => produce less distortion due to quantiation [See details in Design roject on Speech] UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [38] UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [39] 7

8 Hybrid Coding Code-Excited Linear redictive Coding (CEL) Hybrid between LC and waveform coding LC Residual Coding: encode and slowly update LC coefficients, and send the LC residual (e.g. encoded using Vector Quantiation) Advantages: Free from quality degradation due to source modeling Low-frequency waveform is exactly reproduced Spectral information of the entire frequency range is preserved No need of pitch period estimation and voiced/unvoiced decision Multipulse-Excised Linear redictive Coding (MC) Do not distinguish voiced/unvoiced sound explicitly Code-Excited Linear redictive Coding (CEL) Replace the multi-pulses of MC with vector-quantied sequences based on long-term prediction of periodicity and short-term prediction Figure 6.3 of Furui s boo UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [40] UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [43] Speech Quality vs. Transmission Rate Comparison of Different Speech Coding Tech. Figure 6. of Furui s boo Table 6. of Furui s boo UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [45] UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [46] 8

9 ut Together: A Digital Telephone System Figure 1.3 of Quatieri s boo Summary: Main Issues in Speech rocessing UMC ENEE408G Slides (created by M.Wu & R.Liu 00) 8H and 8-bit per sample for telephone speech => 64bps Anti-aliasing filter before sampling Non-uniform quantiation (e.g., through µ-law or A-law companding ~ signal compression-expansion) UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [47] UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [48] UMC ENEE408G Slides (created by M.Wu 00) Summary of Today s s Lecture Speech production Speech analysis and coding This wee s Lab session: Design roject 3 Speech Already have bacground to do art I & II Can play with art III, IV-1, and V Start woring on art VI (ocet C) Next lecture: speech synthesis and recognition UMC ENEE408G Slides (created by M.Wu & R.Liu 00) Assignments The ast, resent, and Future of Speech rocessing IEEE Signal rocessing Magaine, May 1998 Hard copy handout => Read Section I, II, III reparing for Friday Lab Go over art I and II before coming to lab Already have bacground to do art I & II Can play with art III, IV-1, and V Start woring on art VI (ocet C) Team-up and your team information to Instructor and TA by Thursday 5pm UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [49] UMC ENEE408G Capstone -- Multimedia Signal rocessing Lec6 Speech roc. [50] 9

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