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Detection of Burst Onset Landmarks in Speech Using Rate of Change of Spectral Moments A. R. Jayan P. S. Rajath Bhat P. C. Pandey { arjayan , rajathbhat , pcpandey }@ ee.iitb.ac.in EE Dept, IIT Bombay 30 th January, 2011. PRESENTATION OUTLINE. 1. Introduction  Speech landmarks

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Detection of Burst Onset Landmarks in Speech Using Rate of Change of Spectral Moments


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    1. Detection of Burst Onset Landmarks in Speech Using Rate of Change of Spectral Moments A. R. Jayan P. S. Rajath Bhat P. C. Pandey {arjayan, rajathbhat, pcpandey}@ee.iitb.ac.in EE Dept, IIT Bombay 30th January, 2011

    2. PRESENTATION OUTLINE 1. Introduction  Speech landmarks  Landmark detection  Clear speech  Automated speech intelligibility enhancement 2. Methodology  Band energy parameters  Spectral moments  Rate of change function 3. Evaluation and results  VCV utterances  Sentences 4. Conclusion

    3. 1. INTRODUCTION Speech landmarks Regions, associated with spectral transitions, containing important information for speech perception Landmarks and related events [Park, 2008]

    4. Landmark detection Processing  Extraction of parameters characterizing the landmark  Computation of the rate of change (ROC) of parameters  Locating the landmark using ROC(s) Applications  Intelligibility enhancement  Speech recognition  Vocal tract shape estimation

    5. Clear speech  Speech produced with clear articulation when talking to a hearing-impaired listener, or in a noisy environment More intelligible for ▪ Hearing impaired listeners (~17% higher, Picheny et al.,1985) ▪ Listeners in noisy environments (Payton et al., 1994) ▪ Non-native listeners (Bradlow and Bent, 2002) ▪ Children with learning disabilities (Bradlow et al., 2003)  Pronounced acoustic landmarks

    6. Example: ‘The book tells a story’ (Recordings from http://www.acoustics.org/press/145th/clr-spch-tab.htm) Conv. Clear

    7. Automated speech intelligibility enhancement Automated detection of landmarks  High detection rate with low false detections  Good temporal accuracy (5-10 ms)  Computational efficiency Modification of speech characteristics Intensity / duration / spectral modifications around landmarks with minimal perceptual distortions of the acoustic cues in the speech signal

    8. Problems in stop consonant perception  Transient sound with low intensity  Severely affected by noise / hearing impairment Stop landmarks: Closure  Burst onset  Onset of voicing Example: /apa/

    9. Some of the earlier landmark detection techniques  Liu (1996): Rate-of-rise measures of parameters from a set of fixed spectral bands (Speech recognition, g, s, b landmarks, 80 TIMIT sentences, detection rate: 84 % at 20-30 ms, 50 % at 5-10 ms)  Salomon et al. (2002): Temporal parameters related to periodicity, envelope, spectral fine structure (Speech recognition, onsets and offsets of vowels, sonorants, & consonants, 120 TIMIT sentences, detection rate: 90 % at 20 ms)  Sainath and Hazan (2006): Sinusoidal model parameters (Speech segmentation,453 TIMIT sentences, word error rates: 20 % )  Niyogi & Sondhi (2002): Stop landmark detection using total energy, energy above 3 kHz & Wiener entropy(Speech recognition, stop consonants, 320 TIMIT sentences,detection rate: 90 % at 20 ms)  Jayan & Pandey (2009): Stop landmark detection using GMM parameters(Speech enhancement, 50 TIMIT sentences, detection rate: 73 % at 5 ms)

    10. Improving landmark detection Parameters ▪ Capturing spectral transitions ▪ Adaptation to speech variability Rate of change measure ▪ Range of parameter variations ▪ Correlation among parameters  Adaptive time steps ▪ Small time step for abrupt variations ▪ Large time step for slow variations Objective of the present investigation Detection of burst landmarks for automated intelligibility enhancement

    11. 2. METHODOLOGY • Band energy parameters • Log of spectral peaks in three bands • ▪ b1: 1.2-2.0 kHz ▪ b2: 2.0-3.5 kHz ▪ b3: 3.5-5.0 kHz • Mag. spectrum (10 kHz sampling) computed using 512-point DFT, 6 ms Hanning window, 1 frame per ms, and smoothed by 20-point moving average. • Smoothed mag. spectrum X(n, k) used for calculating log of spectral peak in band i n= time index, k=frequency index

    12. Example:Band energy parameters for /aga/ (a) Speech waveform (b) Band energy's Time (ms)

    13. Spectral momentsNormalized spectrum n= time index, k=frequency index, N =DFT size  Centroid :frequency of energy concentration  Variance :spread of energy around the centroid  Skewness :measure of spectral symmetry  Kurtosis :measure of spectral peakiness

    14. Example:Band energy parameters & spectral moments for /aga/ (a) Waveform (b) (c) (d) Time (ms)

    15. Measures of rate of change ●First difference based rate of change (ROC) K = time step ● Mahalanobis distance based rate of change (ROC-MD) A single measure indicative of the overall variation, taking care of parameter range and correlation effects y(n) = parameter set at time n K = time step  = covariance matrix, pre-calculated using the parameter set from segments with energy above a threshold

    16. Detection of voicing offset and onset ▪ Band energy in 0-400 Hz ▪ ROC(n) computed with time step 50 ms ▪ Voicing offset [g-] : ROC(n)  -12 dB ▪ Voicing onset [g+] : ROC(n)  +12 dB Burst onset landmark detection Most prominent peak in the ROC-MD(n) between g- and g+ Example /aga/ (a) Waveform (b) ROC-MD (c) ROC Time (ms)

    17. 3. EVALUTATION & RESULTS Effects of rate of change functions & parameters on burst detection ROC and parameters 1)ROC(BE):Sum of normalized ROCs of [Eb1, Eb2, Eb3] 2)ROC-MD(BE): ROC-MD of [Eb1, Eb2, Eb3] 3)ROC-MD(SM): ROC-MD of [Fc, F,Fk , Fs] 4)ROC-MD(BE,SM): ROC-MD of [Eb1, Eb2, Eb3, Fc , F , Fk , Fs] Material:VCV utterances, TIMIT sentences Time steps:3, 6 ms Temporal accuracies:3, 5, 10, 15, 20 ms

    18. VCV utterances ▪ 6 stop consonants (b, d, g, p, t, k) ▪ 3 vowel contexts (a, i, u) ▪ 10 speakers (5 M, 5 F) ▪ 180 tokens

    19. TIMIT Sentences ▪ 5 speakers (2 M, 3 F) ▪ 10 sentences from each speaker ▪ 238 tokens

    20. 4. CONCLUSION  Increase in time steps reduced detection accuracy.  Mahalanobis distance based ROC was more effective than first-difference based rate of change.  Spectral moments were useful as additional parameters in improving burst-onset detection.

    21. Thank you