Applications

EARs, 22-23 August 2011

Stanley Wenndt, PhD

AFRL/RIGC

Rome Research Site

2April 19, 2023

Familiar Speaker Recognition

• Two motivations– Finish MS Neuroscience degree

• Needed 700-level NEU course, Ind Study only option

– Speech Power versus Speech Intelligibility• Gerber 1974

– What about SID

Frequency Range (Hz)

% Power % Intelligibility

0-500 60 5500-1000 35 351000-2000 3 352000-4000 1 134000-8000 1 12

3April 19, 2023

Audio Data

• In-House Database– Longitudinal study (20 sessions over 3 years)– 65 subjects

• 25 (20 males, 5 females) connected to the Audio Group

– Read, Digits, Short Sentences, Conversations• 10 Short Sentences

– Two intonations• Let’s go skiing today.

– Visual and audible cue• Natural elicitation

• Shortfalls (hindsight)– Unequal Sentences– Different degrees of familiarity between listeners/speakers

4April 19, 2023

Listening Experiments

• Session 1 – Pure Tone Test• Session 2 –Familiarization with Test Set-up• Session 3 – Clean• Session 4 – 0-1K Hz, -20 dB, Speech shaped, add WGN• Session 5 - 1-2K Hz, -20 dB, Speech shaped, add WGN• Session 6 - 2-3K Hz, -20 dB, Speech shaped, add WGN• Session 7 - 3-4K Hz, -20 dB, Speech shaped, add WGN• Session 8 – 0-4K Hz, 0 dB, Speech shaped, add WGN• Session 9 - Clean• Session 10 - Whispered• Session 11 – Time-reversed

5April 19, 2023

Listening Experiments

• Results reported in 2 groups– Normal Hearing– Hearing Deficit

• Hard to draw conclusions from 2nd group– Don’t know severity of hearing loss

• Experiments are a rough 1st pass– 10 SID Listening Sessions– Analyze data– Learn from mistakes

6April 19, 2023

Listening Experiments

Group Clean 0-1K 1K-2K 2K-3K 3K-4K Clean

Normal 90.0 82.2 80.9 76.0 79.1 94.9

HD 73.3 62.0 49.3 50.0 58.0 73.0

Clean 0-1K 1-2K 2-3K 3-4K Clean45

55

65

75

85

95

Normal ListenersListeners with Hearing Deficit

7April 19, 2023

Current Research

• Data Analysis– Difficult to compare between sessions

• Is the performance statistically different

– Between group, within group?

– Current data analysis is focused on individual sentences• Let’s go skiing today.

– Same phonetic content

– Same noise (or lack of)

– Same intonation

– Same session

– Main variable is the speaker

• Formants, shimmer, jitter, energy, etc

8April 19, 2023

“Male 8”

• Most easily recognized voice• Except for Session 6

– 2K-3K noise• Currently, we build models the same

– Good or bad?• Can we figure out what is unique or not unique

about and individual’s voice?

Session 2 Clean 0-1K 1K-2K 2K-3K 3K-4K 0-4K Clean Whis Rev

“Male 8” 35 36 36 33 16 33 34 35 34 33