Perceptual Linear Predictive Analysis of Speech

Hynek Hermansky, Speech Technology Laboratory, J. Acoustical Society of America, April 1990

報告 : 張志豪

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Outline

• Linear Prediction Coding

• Mel-scale Frequency Cepstral Coefficients

• Perceptual Linear Predictive

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Introduction

• Feature Extraction– Speech Production Model

• Linear Prediction Coding

– Speech Perception Model• Mel-scale Frequency Cepstral Coefficients

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Linear Prediction Coding

• Property– Approximates the areas of high-energy concentration while smoothing

out the fine harmonic structure and other less-relevant spectral details.

– The approximated high-energy spectral areas often correspond to the resonance frequencies of the vocal tract (formants).

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Linear Prediction Coding

• Autocorrelation– Levinson-Durbin Recursion

• Impulse Response

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Time domain Time domain Frequency domain

Speech LPC Speech and LPC

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Linear Prediction Coding

• Disadvantage– LPC approximates speech equally well at all frequencies of the

analysis band. This property is inconsistent with human hearing. Beyond about 800Hz, the spectral resolution of hearing decreases with frequency.

– The amplitude levels typically encountered in conversational speech, hearing is more sensitive in the middle frequency range of the audible spectrum.

– The spectral details of speech are not always preserved or discarded by LPC analysis according to their auditory prominence.

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Mel-scale Frequency Cepstral Coefficients

• Mel-scale– 在低頻部分 , 人耳感受是比較敏銳 – 在高頻部分 , 人耳的感受就會越來越粗糙 – 人耳對於頻率的感受事呈對數變化的

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Mel-scale Frequency Cepstral Coefficients

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Mel-scale Frequency Cepstral Coefficients

• Discrete cosine transform – 由 frequency domain 轉回 time domain– frequency of frequency

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MFCC & LPC

• Mel-scale Frequency Cepstral Coefficients – Advantage

• 強調語音頻譜上的特性 , 即使在有雜訊干擾的環境下 , 仍能維持較佳的辨識率– Disadvantage

• 運算量較大

• Linear Prediction Coding– Advantage

• 運算量小– Disadvantage

• 未考慮語音頻譜上的特性 , 辨識率隨著雜訊增加而下降

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Perceptual Linear Predictive

MFCC

LPC

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Perceptual Linear Predictive

• Equal-Loudness Preemphasis2 6 4

2 6 2 9 6 26

( 56.8 10 )( )( 6.3 10 )( 0.38 10 )( 9.58 10 )

w wE ww w w

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Perceptual Linear Predictive

• Equal-Loudness Preemphasis (count.)– 與預強的效果相同 ?

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x 109 Filter Bank

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Frequency domain Frequency domain

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Perceptual Linear Predictive

• Intensity-Loudness Power Law–

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x 109 Equal-Loudness

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Intensity-Loudness

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Perceived loudness, L(w), is approximately the cube root of the intensity, I(w)

L(w) = I(w)

Frequency domain Frequency domain

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Perceptual Linear Predictive

• Intensity-Loudness Power Law (count.)– Power spectrum 不需要再開平方

• ek = (float)sqrt((double)(t1*t1 + t2*t2));

– Filter bank 後的值不需要取 log• bins[bin] = log((double)t1);

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Perceptual Linear Predictive

• Inverse Discrete Fourier Transform– 由 frequency domain 轉回 time domain

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Intensity-Loudness

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Frequency domain Time domain

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Perceptual Linear Predictive

• Autoregressive Modeling (LPC)

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Experiment

　 3 6 9

MFCC 54.21 55.11 55.37

PLP_05 39.01 39.32 39.88

PLP_10 52.55 53.02 53.02

PLP_12 53.79 54.49 54.94

PLP_14 53.62 53.94 54.27

*PLP_12 31.65 32.08 32.03

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Thanks

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Thanks

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Choice Of The Order Of The Autoregressive PLP Model

• Introduction

• Spectral distortion measure of PLP

• Single-frame phoneme identification

• Isolated-word identification

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Choice Of The Order Of The Autoregressive PLP Model

• Introduction – With increasing model order the spectrum of the all-pole model

asymptotically approaches the auditory spectrum.

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Choice Of The Order Of The Autoregressive PLP Model

• Spectral Distortion Measure of PLP – group-delay distortion measure

• The spectral peaks of the model are enhanced and its spectral slope is suppressed.

• The group-delay metric is more sensitive to distance between narrow peaks.

• The group-delay measure is more sensitive to the actual value of the spectral peak width.

– Exponential measure• Allows for various degrees of peak enhancement.

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• Single-Frame Phoneme Identification – As is evident, the PLP identification accuracy increases up to

about the 5th order of the autoregressive model and then starts decreasing with further increases in the model order.

Choice Of The Order Of The Autoregressive PLP Model

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Choice Of The Order Of The Autoregressive PLP Model

• Isolated-Word Identification

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Choice Of The Order Of The Autoregressive PLP Model

• Discussion– The advantage of the PLP over the LP is that it allows for the

effective suppression of the speaker-dependent information by choosing the particular model order.

– The linguistically relevant speaker-independent cues lie in the gross shape of the auditory spectrum. This gross shape can be characterized by the one or two spectral peaks of the 5th-order PLP model.

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PLP and Human Hearing

• Introduction

• Formant Frequency Changes

• Sensitivity to Bandwidth Changes

• Sensitivity to Spectral Tilt

• Sensitivity to F0

• Discussion

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PLP and Human Hearing

• Introduction– The first three formant frequencies is approximately constant in

relative frequency. The LP analysis is in conflict with it.

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PLP and Human Hearing

• Formant Frequency Changes

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PLP and Vowel Perception

• Introduction

• The effective second formant

• Spectral peak integration theory

• The significance of the bandwidth B2

• Discussion

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