SLPA 456 Exam 4

studied byStudied by 0 people
0.0(0)
get a hint
hint

An analog signal is _____ and __ _______

1 / 163

Tags and Description

164 Terms

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

  1. Filtering

  2. Digitization

  3. Frame Selection

  4. Windowing

  5. Short-term analysis

    1. 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

Explore top notes

note Note
studied byStudied by 3 people
Updated ... ago
5.0 Stars(1)
note Note
studied byStudied by 34 people
Updated ... ago
5.0 Stars(1)
note Note
studied byStudied by 67 people
Updated ... ago
5.0 Stars(1)
note Note
studied byStudied by 10 people
Updated ... ago
5.0 Stars(2)
note Note
studied byStudied by 5 people
Updated ... ago
4.5 Stars(2)
note Note
studied byStudied by 6 people
Updated ... ago
5.0 Stars(1)
note Note
studied byStudied by 2 people
Updated ... ago
5.0 Stars(1)
note Note
studied byStudied by 181259 people
Updated ... ago
4.8 Stars(731)

Explore top flashcards

flashcards Flashcard23 terms
studied byStudied by 1 person
Updated ... ago
5.0 Stars(1)
flashcards Flashcard21 terms
studied byStudied by 2 people
Updated ... ago
5.0 Stars(1)
flashcards Flashcard24 terms
studied byStudied by 9 people
Updated ... ago
5.0 Stars(2)
flashcards Flashcard101 terms
studied byStudied by 239 people
Updated ... ago
5.0 Stars(3)
flashcards Flashcard111 terms
studied byStudied by 6 people
Updated ... ago
5.0 Stars(1)
flashcards Flashcard30 terms
studied byStudied by 1 person
Updated ... ago
5.0 Stars(1)
flashcards Flashcard57 terms
studied byStudied by 91 people
Updated ... ago
5.0 Stars(1)
flashcards Flashcard34 terms
studied byStudied by 4 people
Updated ... ago
5.0 Stars(1)