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A Speaker Pruning Algorithm for Real-Time Speaker Identification

AVBPA 2003 Guildford, UK, June 9-11, 2003. A Speaker Pruning Algorithm for Real-Time Speaker Identification. University of Joensuu, FINLAND Department of Computer Science. Tomi Kinnunen, Evgeny Karpov, Pasi Fränti. Abstract. Speaker identification task is computationally very expensive

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A Speaker Pruning Algorithm for Real-Time Speaker Identification

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  1. AVBPA 2003 Guildford, UK, June 9-11, 2003 A Speaker Pruning Algorithm for Real-Time Speaker Identification University of Joensuu, FINLAND Department of Computer Science Tomi Kinnunen, Evgeny Karpov, Pasi Fränti

  2. Abstract • Speaker identification task is computationally very expensive • Most computation originates from calculating the matching scores • Proposed method: drop out unlikely speakers “on the fly” • Reduced computation time with slightly increased error rate

  3. VQ-Based Speaker Identification Speaker model database Unknown voice Loop over the whole database C1 C2 C3 ... ... ... Feature extraction X Ci Ci Matching ... ... ... { D(X,C1),…,D(X,Ci), …,D(X,CN) } CN Select minimum

  4. Match Score Saturation

  5. Towards Speaker Pruning ... • Only a few vectors is enough to rule out most of the speakers • Confidence increases when more vectors are processed Speaker pruning: Drop the unlikely speakers out from competetion when more data arrives  No more distance calculations needed for the pruned speakers

  6. 1st pruning 2ndpruning 3rd pruning Decision Illustration of Pruning Unknown speakers voice sample

  7. Variant 1: Static Pruning Idea: Maintain an ordered list of match scores, and prune out K worst speakers Let C = {C1,…,CN} be the set of all speaker models ; Let X = Ø ; WHILE (C ≠ Ø AND vectors left in input buffer) DO Insert M new vectors from input buffer to set X ; Re-evaluate dissimilarities D(X, Ci) for all Ci in C ; Remove K most dissimilar models from C ; END RETURN arg mini { D(X, Ci) | Ci ЄC } ;

  8. Variant 2: Adaptive Pruning Idea: determine a pruning threshold θ from the distribution of active speakers distances Let C = {C1,…,CN} be the set of all speaker models ; Let X = Ø ; WHILE (C ≠ ØAND vectors left in input buffer) DO Insert M new vectors from input buffer to set X ; Re-evaluate dissimilarities D(X, Ci) for all Ci in C ; Compute μ and σ of the distribution { D(X, Ci) | Ci ЄC }; Let θ = μ + η σbe the pruning threshold ; Remove all speakers i from C satisfying D(X, Ci) > θ ; END RETURN arg mini { D(X, Ci) | Ci ЄC } ;

  9. Illustration of Adaptive Pruning Histograms of matching scores as a function of time Pruned speakers Frequency of occurrence Match score (distance)

  10. μ μ+ησ Parameters of the Variants • Static pruning: Number of speakers to prune at each interval • Adaptive pruning: The η - parameter in the pruning threshold • It is assumed that distances follow a Gaussian distribution with mean μand variance σ2 •  ηspecifies a certain confidence interval

  11. Experimental Setup • TIMIT-corpus: • N = 630 American English speakers, clean speech • Sample rate Fs = 8 kHz, 16 bps resolution • Pre-processing and MFCC feature extraction : • - Silence removed, pre-emphasis H(z) = 1 - 0.97z-1 • - 30 ms Hamming window, shifted by 10 ms • - 27 triangular bandpass filters spaced equally on mel-scale • - 0th cepstral coefficient excluded • Speaker models : • Codebooks of 64 vectors by Linde-Buzo-Gray algorithm • Training data: 8.8 seconds / speaker (without silence)

  12. Evaluation Criteria • Identification error rate + Avg. identification time per speaker •  Combined: error rate as a function of time • Reference point: • Full-search (no speaker pruning) achieves 0.15 % error rate (one misclassified speaker) on average in 230 seconds ( 4 minutes)

  13. Error < 0.5 % in 50 seconds Static Pruning [Full search: 0.15 % in 230 seconds]

  14. Error < 0.5 % in 25 seconds Adaptive Pruning [Full search: 0.15 % in 230 seconds]

  15. Static: 5.5 % Adaptive:0.5% Static: 0.5 % Adaptive: 0.18% 25 s. 50 s. Comparison of the Variants [Full search: 0.15 % in 230 seconds]

  16. Conclusions • Speed-up ratio 9:1 with only minor degration in accuracy • Full search: 629/630 correct in 220 seconds • Static pruning: 595/630 correct in 25 seconds • Adaptive pruning: 627/630 correct in 25 seconds • Adaptive variant outperforms static variant • Selection of the parameters not crucial •  Easy to apply in practice • Both variants are straightforward to implement • Easily extendable to other models (e.g. GMM)

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