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Predicting Speech Intelligibility

Predicting Speech Intelligibility

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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