Studied by 0 people

0.0(0)

Get a hint

Hint

1

An analog signal is _____ __and__ __ _______

Continuous and time-varying

New cards

2

Speech is an example of a _________ signal

Analog

New cards

3

A digital signal is ______.

Discrete

New cards

4

3 main parameters of sound

frequency, time, and amplitude

New cards

5

3 types of errors that can occur during ADC

Jitter, Quantization noise, and Aliasing

New cards

6

Jitter:

deviation in periodicity

can be a result of irregularities in sampling rate

New cards

7

Quantization noise:

deviation in amplitude measures

can be result of rounding errors in process of quanization

New cards

8

Aliasing:

distortion due to misidentification of frequency

can be result of inappropriate sampling rate

New cards

9

Digital Signal Processing (DSP)

Pre-Processing of a digital signal

New cards

10

Steps of DSP

Speech Signal

Filtering

Digitization

Frame Selection

Windowing

Short-term analysis

Graphic display or numeric output

New cards

11

Elements of filtering

Pre-emphasis, presampling

New cards

12

Elements of digitization

time sampling, quantization

New cards

13

elements of Frame Selection

Frame length, frame overlap

New cards

14

elements of windowing

tapering function

New cards

15

elements of short-term analysis

FFT, LPC, Cepstrum

New cards

16

elements of Graphic display or numeric output

spectogram, spectrum, other

New cards

17

Goal of filtering:

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

New cards

18

Pre-Sampling:

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

New cards

19

Aliasing

underrepresentation of the sampling rate because the original signal is underrepresented

New cards

20

Example of anti-alias filter:

DC Off-set

New cards

21

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

New cards

22

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.

New cards

23

Frame Selction/Windowing:

process of selecting which parts of signal to be analyzed

New cards

24

Window/Frame:

the portion of the signal selected to perform an analysis on

New cards

25

Windowing option examples:

Rectangle

Bartlett

Hanning

Hamming*

Blackman

Gauss

New cards

26

How is ABR recording windowed?

Based on a TIME-specific analysis!

New cards

27

Types of graphic displays of acoustic data:

Waveform, Spectrum, Spectogram, Profiles or contours

New cards

28

Dimensions of a waveform:

Amplitude by time

New cards

29

Types of waveforms (temporal analysis)

raw, envelope

New cards

30

Dimensions of a spectrum:

Amplitude by frequency

New cards

31

Types of spectrums (Spectral Analysis)

Fast Fourier Transform, Linear Prediction Coding, Cepstrum

New cards

32

Dimensions of spectogram:

Amplitude by frequency by time

New cards

33

Types of spectrograms (speech (complex) analysis

Conventional, countour, waterfall

New cards

34

Dimensions of Profiles or contours

Parameter by time

New cards

35

Types of profiles/contours:

f0 trace (pitch contour), intensity profile

New cards

36

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

Waveform

New cards

37

What information can you analyze from a waveform?

Fundamental frequency

Perturbation Measures

Signal-to-noise ratio

Voice onset time

Vowel duration

Envelope

New cards

38

Fundamental Frequency:

frequency at which a system oscillates/resonates freely

New cards

39

Signal Processing Strategy used to get fundamental frequency:

Pitch determination algorithm (PDA) or pitch extractor

New cards

40

Temporal methods used by PDA:

Zero crossing

Peak Picking

Auto correlation (most modern)

New cards

41

Zero Crossing:

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

New cards

42

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

New cards

43

Perturbation measures:

3 types we can measure

jitter

shimmer

signal to noise ratio

New cards

44

Perturbgation:

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

New cards

45

Jitter:

variability in the fundamental period of phonation

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

New cards

46

Jitter Percent:

obtained by dividing absolute jitter value by mean fundamental frequency period

New cards

47

Shimmer:

variability of amplitude of successive cycles of waveform

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

New cards

48

Shimmer Percent:

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

New cards

49

Signal to Noise Ratio:

Ratio of Periodic energy to aperiodic energy in the voice waveform

New cards

50

With NO background noise, SNR = _________

The intensity of the signal

New cards

51

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

A negative value

New cards

52

Voice Onset Time:

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

New cards

53

Vowel Duration:

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

aka vowel steady rate

New cards

54

Envelope:

overall profile of waveform

New cards

55

Spectral (frequency based) analysis operate directly on a _______

spectrum

New cards

56

Commonly used software for spectral analyses:

Audacity

PRAAT

Computerized Speech Lab (CSL)

New cards

57

Which spectral analysis software has few spectral analyses options?

Audacity

New cards

58

Which spectral analysis software is most widely used acoustic freeware?

PRAAT

New cards

59

Which spectral analysis software is professional software?

Computerized Speech Lab

New cards

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)

New cards

61

Fourier Transform

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

New cards

62

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

New cards

63

Fast Fourier Transform

optimized algorithm to calculate DFT

all speech analyses software packages have an implementation of FFT

New cards

64

Linear Predictive Coding

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

New cards

65

Cepstrum

A fourier transfer performed on the spectrum

inverse/transposition of spectrum

New cards

66

What is a cepstrum useful in investigating?

Periodicity/ rate of change of a signal

New cards

67

Terms associated with Spectrum vs. Cepstrum:

Spectrum: frequency and amplitude → Harmonics → filtering

Cepstrum: Quefrency and amplitude → Rahmonics → liftering

New cards

68

2 important features of a cepstrum:

preserves magnitude information about the signal and discard phase related info

emphasizes periodic nature of harmonics

New cards

69

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

New cards

70

What do rahmonics show?

correlates to the perceptual “quality” measures of voice

New cards

71

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)

New cards

72

Practically, when is mel frequency cepstral coefficients most useful?

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

New cards

73

How to obtain formants:

by using any spectral analysis method

New cards

74

Two main characteristics of formants:

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

resonance of vocal tract

New cards

75

Which formants are typically used to describe most speech sounds?

F1 and F2

New cards

76

For vowels, what does F1 describe?

Tongue Height

New cards

77

For vowels, what does F2 describe?

tongue position

New cards

78

Formant Amplitude:

Relative amplitude of formants in a formant pattern?

New cards

79

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.

New cards

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

New cards

81

Measurements based on “dynamic” aspect of formants:

Formant Transition

Formant Locus

Formant Slope

Locus equation

New cards

82

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

Constriction

New cards

83

Sonorant Consonants

NO pressure build up at constriction

New cards

84

Nasal Consonants

lower the velum allowing airflow in nasal cavity

New cards

85

Continuant Consonants

do not block airflow in oral cavity

New cards

86

Resonators:

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

New cards

87

Natural Frequency of a resonator is based on _____.

Length and diameter of the vocal tract

New cards

88

Relation of harmonic frequencies to resonating frequency

If close to resonating frequency: will be amplified

If far from resonating frequency: will be dampened

New cards

89

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

They tend to boost each other’s amplitude

New cards

90

Formant Bandwidth:

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

New cards

91

Which graphic representation can you find formant bandwidth?

on a Spectrum

New cards

92

Practical use of formant space measurements:

represents maximum working space of a talker

representative of maximum performance

New cards

93

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

New cards

94

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

New cards

95

Primary use of LTF:

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

New cards

96

When is speech dynamic?

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

New cards

97

Formant transition:

relative shange from a vowel to a consonant

New cards

98

What speech sounds are formant transitions specifically associated with?

stop consonants

New cards

99

Formant locus:

characteristic value for each place of consonant articulation

** helpful to judge phonemes and speech intelligibility

New cards

100

Formant slope:

the change in formant frequency over an interval of formant transition

** helpful in studying speech intelligibility in dysarthric speakers

New cards