Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences

COLLEGE OF ENGINEERING

UC Berkeley

   

Research Projects

Parallel MLP Feature Extraction for Speech Recognition

Nelson Morgan, Chris Oei1 and Adam Janin2

International Computer Science Institute and Microsoft/Intel Parallel Computing Laboratory

There is much parallelism inherent in speech recognition techniques, particularly for the systems built at ICSI. The front-end for acoustic feature extraction performs spectral analysis of the audio signal and classifies phonetic units with a multi-layer perceptron (neural network); the bulk of this computation is dense matrix-matrix multiplication, for which we can exploit BLAS subroutines. Using multi-threaded BLAS libraries enabled significant speedup on MLP forward pass and backpropagation training on a multicore CPU architecture. We hope to gain even more speedup by exploiting the greater parallelism of a GPU architecture. We are also investigating feature extraction schemes in which many MLPs operate independently in parallel.

1ICSI
2ICSI