Ron M. Hecht, Elad Noor, Gil Dobry, Y. Zigel, Aharon Bar-Hillel, Naftali Tishby
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引用次数: 4
Abstract
The common approaches to feature extraction in speech processing are generative and parametric although they are highly sensitive to violations of their model assumptions. Here, we advocate the non-parametric Information Bottleneck (IB). IB is an information theoretic approach that extends minimal sufficient statistics. However, unlike minimal sufficient statistics which does not allow any relevant data loss, IB method enables a principled tradeoff between compactness and the amount of target-related information. IB's ability to improve a broad range of recognition tasks is illustrated for model dimension reduction tasks for speaker recognition and model clustering for age-group verification.
期刊介绍:
The IEEE Transactions on Audio, Speech and Language Processing covers the sciences, technologies and applications relating to the analysis, coding, enhancement, recognition and synthesis of audio, music, speech and language. In particular, audio processing also covers auditory modeling, acoustic modeling and source separation. Speech processing also covers speech production and perception, adaptation, lexical modeling and speaker recognition. Language processing also covers spoken language understanding, translation, summarization, mining, general language modeling, as well as spoken dialog systems.