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Applications. EARs, 22-23 August 2011. Stanley Wenndt, PhD AFRL/RIGC Rome Research Site. 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

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applications

Applications

EARs, 22-23 August 2011

Stanley Wenndt, PhD

AFRL/RIGC

Rome Research Site

familiar speaker recognition
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
audio data
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
listening experiments
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
listening experiments1
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
current research
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
male 8
“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?
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