# SLPA 456 Exam 4

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An analog signal is _____ and __ _______

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### 164 Terms

1

An analog signal is _____ and __ _______

Continuous and time-varying

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2

Speech is an example of a _________ signal

Analog

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3

A digital signal is ______.

Discrete

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4

3 main parameters of sound

frequency, time, and amplitude

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3 types of errors that can occur during ADC

Jitter, Quantization noise, and Aliasing

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Jitter:

deviation in periodicity

• can be a result of irregularities in sampling rate

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Quantization noise:

deviation in amplitude measures

• can be result of rounding errors in process of quanization

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Aliasing:

distortion due to misidentification of frequency

• can be result of inappropriate sampling rate

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9

Digital Signal Processing (DSP)

Pre-Processing of a digital signal

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Steps of DSP

Speech Signal

1. Filtering

2. Digitization

3. Frame Selection

4. Windowing

5. Short-term analysis

1. Graphic display or numeric output

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Elements of filtering

Pre-emphasis, presampling

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Elements of digitization

time sampling, quantization

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elements of Frame Selection

Frame length, frame overlap

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elements of windowing

tapering function

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elements of short-term analysis

FFT, LPC, Cepstrum

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elements of Graphic display or numeric output

spectogram, spectrum, other

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Goal of filtering:

retain wanted parts of the signal while removing parts that do not necessarily provide any information

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Pre-Sampling:

“anti-aliasing” - applying filters that block frequencies above the Nyquist frequency for that sample

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Aliasing

underrepresentation of the sampling rate because the original signal is underrepresented

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Example of anti-alias filter:

DC Off-set

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Pre-Emphasis:

Equalizes (boosts weaker) energies over a specified range of frequencies so important aspects of signal have sufficient energy to accurately capture within the quantization bits available

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Practical example of filtering in Aud and SLP

Measuring Auditory Brainstem Responses (ABR)

Removes:

• direct current (DC) signals from other electronic equipment

• 60 Hz hum from alternating current (AC) power sources

• background EEG activity, unwanted brain activity

• uses pre-emphasis method called differential amplification

• boosts level of desired evoked potential response while removing the extra noise.

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Frame Selction/Windowing:

process of selecting which parts of signal to be analyzed

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Window/Frame:

the portion of the signal selected to perform an analysis on

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Windowing option examples:

Rectangle

Bartlett

Hanning

Hamming*

Blackman

Gauss

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How is ABR recording windowed?

Based on a TIME-specific analysis!

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Types of graphic displays of acoustic data:

Waveform, Spectrum, Spectogram, Profiles or contours

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Dimensions of a waveform:

Amplitude by time

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Types of waveforms (temporal analysis)

raw, envelope

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30

Dimensions of a spectrum:

Amplitude by frequency

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Types of spectrums (Spectral Analysis)

Fast Fourier Transform, Linear Prediction Coding, Cepstrum

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Dimensions of spectogram:

Amplitude by frequency by time

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Types of spectrograms (speech (complex) analysis

Conventional, countour, waterfall

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Dimensions of Profiles or contours

Parameter by time

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Types of profiles/contours:

f0 trace (pitch contour), intensity profile

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36

Temporal (time-based) analysis works directly on the ______.

Waveform

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37

What information can you analyze from a waveform?

Fundamental frequency

Perturbation Measures

Signal-to-noise ratio

Voice onset time

Vowel duration

Envelope

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Fundamental Frequency:

frequency at which a system oscillates/resonates freely

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Signal Processing Strategy used to get fundamental frequency:

Pitch determination algorithm (PDA) or pitch extractor

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Temporal methods used by PDA:

Zero crossing

Peak Picking

Auto correlation (most modern)

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Zero Crossing:

counts every time a wave passes through the zero line within a second, then divides by two to obtain the fundamental frequency

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Peak Picking:

Fundamental frequency is derived by identifying wave peaks and counting either the total number of crests or troughs OR total number of peaks in general and dividing by 2

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Perturbation measures:

3 types we can measure

• jitter

• shimmer

• signal to noise ratio

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Perturbgation:

a deviation from truly periodic and regular patterns of vibration of the vocal folds

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Jitter:

variability in the fundamental period of phonation

• reported in an absolute value (ms) or relative value (%)

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Jitter Percent:

obtained by dividing absolute jitter value by mean fundamental frequency period

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Shimmer:

variability of amplitude of successive cycles of waveform

• reported in an absolute value (dB) or relative value (shimmer %)

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Shimmer Percent:

obtained by dividing absolute shimmer value by the mean amplitude of the waveform

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Signal to Noise Ratio:

Ratio of Periodic energy to aperiodic energy in the voice waveform

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50

With NO background noise, SNR = _________

The intensity of the signal

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When background noise is louder than the signal, SNR = ________

A negative value

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Voice Onset Time:

duration of the interval between release of a stop consonant and the onset of vocal fold vibration (vowel production)

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Vowel Duration:

duration of the interval over which the formant pattern (specifically F1 and F2) is stable

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Envelope:

overall profile of waveform

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55

Spectral (frequency based) analysis operate directly on a _______

spectrum

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56

Commonly used software for spectral analyses:

Audacity

PRAAT

Computerized Speech Lab (CSL)

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57

Which spectral analysis software has few spectral analyses options?

Audacity

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58

Which spectral analysis software is most widely used acoustic freeware?

PRAAT

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59

Which spectral analysis software is professional software?

Computerized Speech Lab

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60

Major types of Spectral Analysis:

Fourier Transform: Discrete (DFT) and Fast (FFT),

Linear Predictive Coding (LPC),

Cepstral based analyses,

Mel Frequency Cepstral Coefficients (MFCC)

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Fourier Transform

Decomposes a waveform to reveal its frequency content to convert a waveform to a power spectrum

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Discrete Fourier Transform

Fourier transform of a finite set of discrete samples from the waveform (determined by sampling rate and windowing)

• transforms data from samples into distinct frequency lines within a power spectrum

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Fast Fourier Transform

optimized algorithm to calculate DFT

• all speech analyses software packages have an implementation of FFT

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Linear Predictive Coding

Based on Quazi-periodic nature of speech, by knowing certain parts of the speech signal, other parts can be predicted

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Cepstrum

A fourier transfer performed on the spectrum

• inverse/transposition of spectrum

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What is a cepstrum useful in investigating?

Periodicity/ rate of change of a signal

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Terms associated with Spectrum vs. Cepstrum:

Spectrum: frequency and amplitude → Harmonics → filtering

Cepstrum: Quefrency and amplitude → Rahmonics → liftering

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2 important features of a cepstrum:

• preserves magnitude information about the signal and discard phase related info

• emphasizes periodic nature of harmonics

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What do cepstrum algorithms reveal in a signal?

Converting the signal and finding one formant enables algorithms that help find patterns to find the others

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What do rahmonics show?

correlates to the perceptual “quality” measures of voice

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Mel Frequency Cepstral Coefficients (MFCC)

represent short-term power within a second

• represents frequency bands as evenly spaced whereas cepstrum represents frequency bands linearly

• more representative of human auditory sensitivity (perception of pitch)

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Practically, when is mel frequency cepstral coefficients most useful?

in audio compression and speech recognition systems (eg. HA mapping)

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How to obtain formants:

by using any spectral analysis method

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Two main characteristics of formants:

• peak in spectrum of a vowel sound or energy bands in spectrogram

• resonance of vocal tract

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75

Which formants are typically used to describe most speech sounds?

F1 and F2

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For vowels, what does F1 describe?

Tongue Height

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For vowels, what does F2 describe?

tongue position

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Formant Amplitude:

Relative amplitude of formants in a formant pattern?

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Formant Space:

aka acoustic working space, acoustic vowel space, vowel triangle

• plot of F1 vs F2

• measures speech intelligibility

• several other measures are derived from formant space.

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80

Examples of measures based on (static) formant space:

• vowel space area

• formant centralization ratio

• four vowel articulation index

• Formant centroid

• Vocalic anatomical functional ratio

• long-term formant distribution

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Measurements based on “dynamic” aspect of formants:

• Formant Transition

• Formant Locus

• Formant Slope

• Locus equation

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82

Vowels, glides, and consonants differ in degree of ________.

Constriction

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83

Sonorant Consonants

NO pressure build up at constriction

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Nasal Consonants

lower the velum allowing airflow in nasal cavity

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Continuant Consonants

do not block airflow in oral cavity

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Resonators:

specific state of vocal tract that amplifies frequencies near the natural frequency of that system

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87

Natural Frequency of a resonator is based on _____.

Length and diameter of the vocal tract

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88

Relation of harmonic frequencies to resonating frequency

If close to resonating frequency: will be amplified

If far from resonating frequency: will be dampened

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89

Relationship of two formants when they are close in frequency to one another,

They tend to boost each other’s amplitude

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Formant Bandwidth:

difference (in Hz) between frequencies at +/- 3 dB of the intensity of the center frequency within a formant

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Which graphic representation can you find formant bandwidth?

on a Spectrum

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Practical use of formant space measurements:

represents maximum working space of a talker

• representative of maximum performance

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Vowel Space Area

aka F1-F2 area

• calculated using a specific formula identifying the area of formant space graph

• Used to study variety of speech and voice disorders

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Long term formant distribution (LTF)

• average formant frequency of a given speaker

• calculated by taking average of all formants across all vowels in recorded sample

• used to study variety of speech and voice disorders

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Primary use of LTF:

forensic speaker identification and in studying effects of age and sex on speech

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When is speech dynamic?

when there are changes as a result of consonants embedded along with vowels -- typical running speech

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Formant transition:

relative shange from a vowel to a consonant

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What speech sounds are formant transitions specifically associated with?

stop consonants

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Formant locus:

characteristic value for each place of consonant articulation

** helpful to judge phonemes and speech intelligibility

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Formant slope:

the change in formant frequency over an interval of formant transition

** helpful in studying speech intelligibility in dysarthric speakers

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