82,611 research outputs found
Recognizing Multi-talker Speech with Permutation Invariant Training
In this paper, we propose a novel technique for direct recognition of
multiple speech streams given the single channel of mixed speech, without first
separating them. Our technique is based on permutation invariant training (PIT)
for automatic speech recognition (ASR). In PIT-ASR, we compute the average
cross entropy (CE) over all frames in the whole utterance for each possible
output-target assignment, pick the one with the minimum CE, and optimize for
that assignment. PIT-ASR forces all the frames of the same speaker to be
aligned with the same output layer. This strategy elegantly solves the label
permutation problem and speaker tracing problem in one shot. Our experiments on
artificially mixed AMI data showed that the proposed approach is very
promising.Comment: 5 pages, 6 figures, InterSpeech201
Mixing multi-core CPUs and GPUs for scientific simulation software
Recent technological and economic developments have led to widespread availability of
multi-core CPUs and specialist accelerator processors such as graphical processing units
(GPUs). The accelerated computational performance possible from these devices can be very
high for some applications paradigms. Software languages and systems such as NVIDIA's
CUDA and Khronos consortium's open compute language (OpenCL) support a number of
individual parallel application programming paradigms. To scale up the performance of some
complex systems simulations, a hybrid of multi-core CPUs for coarse-grained parallelism and
very many core GPUs for data parallelism is necessary. We describe our use of hybrid applica-
tions using threading approaches and multi-core CPUs to control independent GPU devices.
We present speed-up data and discuss multi-threading software issues for the applications
level programmer and o er some suggested areas for language development and integration
between coarse-grained and ne-grained multi-thread systems. We discuss results from three
common simulation algorithmic areas including: partial di erential equations; graph cluster
metric calculations and random number generation. We report on programming experiences
and selected performance for these algorithms on: single and multiple GPUs; multi-core CPUs;
a CellBE; and using OpenCL. We discuss programmer usability issues and the outlook and
trends in multi-core programming for scienti c applications developers
Taking advantage of hybrid systems for sparse direct solvers via task-based runtimes
The ongoing hardware evolution exhibits an escalation in the number, as well
as in the heterogeneity, of computing resources. The pressure to maintain
reasonable levels of performance and portability forces application developers
to leave the traditional programming paradigms and explore alternative
solutions. PaStiX is a parallel sparse direct solver, based on a dynamic
scheduler for modern hierarchical manycore architectures. In this paper, we
study the benefits and limits of replacing the highly specialized internal
scheduler of the PaStiX solver with two generic runtime systems: PaRSEC and
StarPU. The tasks graph of the factorization step is made available to the two
runtimes, providing them the opportunity to process and optimize its traversal
in order to maximize the algorithm efficiency for the targeted hardware
platform. A comparative study of the performance of the PaStiX solver on top of
its native internal scheduler, PaRSEC, and StarPU frameworks, on different
execution environments, is performed. The analysis highlights that these
generic task-based runtimes achieve comparable results to the
application-optimized embedded scheduler on homogeneous platforms. Furthermore,
they are able to significantly speed up the solver on heterogeneous
environments by taking advantage of the accelerators while hiding the
complexity of their efficient manipulation from the programmer.Comment: Heterogeneity in Computing Workshop (2014
Improving large vocabulary continuous speech recognition by combining GMM-based and reservoir-based acoustic modeling
In earlier work we have shown that good phoneme recognition is possible with a so-called reservoir, a special type of recurrent neural network. In this paper, different architectures based on Reservoir Computing (RC) for large vocabulary continuous speech recognition are investigated. Besides experiments with HMM hybrids, it is shown that a RC-HMM tandem can achieve the same recognition accuracy as a classical HMM, which is a promising result for such a fairly new paradigm. It is also demonstrated that a state-level combination of the scores of the tandem and the baseline HMM leads to a significant improvement over the baseline. A word error rate reduction of the order of 20\% relative is possible
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