Extracting Posteriors from a Gaussian Mixture Model
نویسنده
چکیده
The design of a system for extracting posterior distributions from a Gaussian mixture model is presented and its implementation on a FPGA is discussed; the system is intended for eventual integration into a processing pipeline for real-time speaker identification. An existing software implementation of the posterior extraction algorithm, written using the Kaldi speech recognition toolkit, was used as the basis of the design, which comprises three primary modules. A detailed description of the microarchitecture for each module is described, as well as the memory layout. The performance and numerical accuracy of the system were evaluated and found to be satisfactory for most applications of interest.
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