6 accepted papers
Neural network based attention modeling has found utility in areas such as visual analysis, speech recognition and more recently speaker recognition. Attention represents a gating (or weighting) function on information and governs how the corresponding statistics are accumulated. In the context of s…
This paper discusses some challenges and opportunities in developing a speaker diarization system for operation on real world call center telephony data. We contrast some of the differences between a standard data set akin to NIST evaluations and those found in call centers. In exploring these diffe…
Many speaker diarization systems operate in an off-line mode. Such systems typically find homogeneous segments and then cluster these segments according to speaker. Such algorithms, like bottom-up clustering, k-means or spectral clustering, generally require the registration of all segments before c…
Automatic age estimation from speech has a variety of applications including natural human-computer interaction, targeted advertising, customer-agent pairing in call centers, and forensics, to mention a few. Recently, the use of i-vectors has shown promise for automatic age estimation. In this paper…
Many state-of-the-art speaker recognition engines use i-vectors to represent variable-length acoustic signals in a fixed low-dimensional total variability subspace. While such systems perform well under seen channel conditions, their performance greatly degrades under unseen channel scenarios. Accor…
Many state-of-the-art i-vector based voice biometric systems use linear discriminant analysis (LDA) as a post-processing stage to increase the computational efficiency in the back-end via dimensionality reduction, as well as annihilate the undesired (noisy) directions in the total variability subspa…