Solving global permutation ambiguity of time domain BSS using speaker specific features of speech signals

Vahid Khanagha (1) , Ali Khanagha

1 GeoStat - Geometry and Statistics in acquisition data

Multidimensional localization of competing speakers using BSS based TDOA estimations, requires the solution of global permutation ambiguity before fusing several TDOA estimates. Since the separation quality of BSS is not perfect, it is not easy to decide which TDOA belongs to which source (specially when the number of speakers grows). We study the robustness of several speaker specific features of speech against dereverberation filtering, by evaluating their capability to recognize perceptually dominant sources in each one of moderately enhanced outputs of the BSS algorithm. We compare the performance of several features in terms of Average Decision Statistic and computational complexity.

Mots clés: Blind Source Separation(BSS); Particle Swarm Optimization(PSO); Multiple Speaker Localization

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Vahid Khanagha, Ali Khanagha. Solving global permutation ambiguity of time domain BSS using speaker specific features of speech signals. 2009 IEEE Symposium on Industrial Electronics and Applications., Oct 2009, Kuala Lumpur, Malaysia. ⟨inria-00438658⟩