introduction sibilant speech is aperiodic. the fricatives /s/, / ʃ /, /z/ and / Ʒ / and the...

SIBILANT SPEECH DETECTION IN NOISEBY: HOSEIN BITARAFSUPERVISOR: DR. NASERSHARIF

INTRODUCTION

Sibilant speech is aperiodic. the fricatives /s/, /ʃ/, /z/ and /Ʒ/ and the

affricatives /tʃ/ and /dƷ/ we present a sibilant detection algorithm

robust to high levels of noise

Gaussian for noisy speech signal

Xk,i = power K = frequency i = time-frame µk,i = mean power

PSD for /ʃ/

Log-likelihood

µk,N1 = µk,N2 = ak

µk,S = ak + bk

Maximizing the log-likelihood

74% of sibilant within 60 and 130 ms. |t| < 30 ms high probability sibilant |t| > 65 ms high probability outside the

sibilant. reduces contribution of the transition region 30 ms < |t| < 65 ms

Maximizing the log-likelihood

Maximizing the log-likelihood

Maximizing the log-likelihood

Estimate noise and siblant

Estimated sibilant mean power

Maximum filter

W = 30

Normalization

To make the estimate independent of the overall speech level

Gaussian Mixture Model

For each frame has two Gaussian mix-ture models (GMMs):

one trained on non-sibilant speech and the other on sibilant speech.

EXPERIMENTS

Filter for1.5 kHz to 8 kHz. The weighting function used for three

Hamming windows

GMMs

The input for the GMMs was a 14-component vector

containing the estimated sibilant power spectrum from

1.5 kHz to 8 kHz every 500 Hz

Result

White Gaussian noise was added to the speech files

it is more difficult to detect sibilants in white noise than in other typical stationary noise

Result

Pmiss = miss probability

Pfa = false alarm probability

Result

Result

CONCLUSIONS

we have presented a sibilant detection algorithm with noise

sibilant mean power estimation stage likelihood ratio of two GMMs, Test in TIMIT . 80% classification accuracy for positive

SNRs.

For Future

it is possible that its classification accuracy could be further improved by applying temporal constraints to the classification decisions.

Thank you