By Noam Shabtai
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Extra resources for Advances in Speech Recognition
Quatieri, T. , and Dunn, R. B. (2000). Speaker verification using adapted Gaussian mixture models. Digital Signal Processing, 10(1):19–41. [Schroeder, 1965] Schroeder, M. R. (1965). New method of measuring the reverberation time. J. Acoust. Soc. , 37(3):409–412. , 2008a] Shabtai, N. , and Zigel, Y. (2008a). The effect of GMM order and CMS on speaker recognition with reverberant speech. In Proc. HSCMA, pages 144–147. , 2008b] Shabtai, N. , and Zigel, Y. (2008b). The effect of room parameters on speaker verification using reverberant speech.
It is neither required nor feasible to process all these states for backend classification, therefore these states were sampled at 25 ms in linear scale from start to the end of simulations and used as training vectors for the classifier. In all these experiments, only the readout neurons were trained whereas the reservoir connectivity remained fixed for generating the reservoir states. The performance of backend feedforward classifier was evaluated with test samples and the best results obtained in different trials are shown in Table 3, 4 and 5.
Cross marks ("x") denote no feature normalization (linear fitting with thick solid line), circles ("o") denote CMS (linear fitting with thick dashed line), and triangles (’Δ’) denote using CMS with variance normalization (linear fitting with thin solid line). Test speech segments were made reverberant by convolution with (a) simulated, and (b) measured RIRs. The Effect of Reverberation on Optimal GMM Order and CMS Performance in Speaker Verification Systems 49 10. References [CHR, 2004] (2004).