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Predicting Speech Intelligibility

Predicting Speech Intelligibility. Where we were…. Model of speech intelligibility Good prediction of Greenberg’s bands Data. Greenberg Bands. Timit Sentences filtered into four narrow spectral channels Task identify speech coded in various channel combinations. Greenberg’s data.

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Predicting Speech Intelligibility

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  1. Predicting Speech Intelligibility

  2. Where we were… • Model ofspeechintelligibility • Goodpredictionof Greenberg’sbands Data

  3. Greenberg Bands • Timit Sentences filtered into four narrow spectral channels • Task identifyspeech codedin various channel combinations

  4. Greenberg’s data • High levels ofintelligibilityfor reducedrepresentation • Intelligibility isnot sum of partsch 2 : 9ch 3 : +9ch23: =60

  5. Hearing threshold

  6. Matching Perceptual Data • R2=0.987 for bands data • Model • Filterbank (32 channels ERB) • Modulation filter (Fc=1kHz) • Mutual information in modulation map/spectrum space • But how well does it compare

  7. SII • ASA Working Group S3-79,in charge of reviewing ANSIS3.5-1997 (“Methods for Calculation of the Speech Intelligibility Index”).http://www.sii.to/ • SII computes intelligibility • Speech Spectrum Level • Equivalent Noise Spectrum Level • Equivalent Hearing Threshold Level [dBHL] • Band Importance function

  8. Predictions for: • Average speech • various nonsense syllable tests where most English phonemes occur equally often • CID-22 • NU6 • Diagnostic Rhyme test • short passages of easy reading material • SPIN

  9. SII predictions for Bands • Hannes Muesch: SII is not designed to work for narrow spectral bands – and it doesn’t… • Bands 1234: prediction 25% ; reality 88%

  10. Reasons for SII failure • SII is a glorified lookup table • computes weighted contribution of individual channels, assumption ‘broad bands’ • Adjacent auditory channels are highly correlated • A contiguous band of 4 channels is • “one information bearing channel”, plus • “three channels with little extra information

  11. How does SII compare • Our algorithm computes ‘information’ not intelligibility • Expect vocabulary size, word type … to make a difference

  12. SII fit

  13. Conclusion • MI based measure marginally better than SII if treated equally,BUT • SII is based on lookup tables, with only small model components (masking, thold) • MI measure is an algorithmic solution • Key Question • Does MI solution generalise? • How to deal with wide bands?

  14. Generalisation • Currently running series of experiments using BKB data • white noise • Greenberg slits

  15. Dealing with correlation • Need to compute the ‘added information’ that extra channels contribute to existing channels – could do with principled solution here

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