  Numerous compression and acceleration strategies have achieved outstanding
results on classification tasks in various fields, such as computer vision and
speech signal processing. Nevertheless, the same strategies have yielded
ungratified performance on regression tasks because the nature between these
and classification tasks differs. In this paper, a novel sign-exponent-only
floating-point network (SEOFP-NET) technique is proposed to compress the model
size and accelerate the inference time for speech enhancement, a regression
task of speech signal processing. The proposed method compressed the sizes of
deep neural network (DNN)-based speech enhancement models by quantizing the
fraction bits of single-precision floating-point parameters during training.
Before inference implementation, all parameters in the trained SEOFP-NET model
are slightly adjusted to accelerate the inference time by replacing the
floating-point multiplier with an integer-adder. For generalization, the
SEOFP-NET technique is introduced to different speech enhancement tasks in
speech signal processing with different model architectures under various
corpora. The experimental results indicate that the size of SEOFP-NET models
can be significantly compressed by up to 81.249% without noticeably downgrading
their speech enhancement performance, and the inference time can be accelerated
to 1.212x compared with the baseline models. The results also verify that the
proposed SEOFP-NET can cooperate with other efficiency strategies to achieve a
synergy effect for model compression. In addition, the just noticeable
difference (JND) was applied to the user study experiment to statistically
analyze the effect of speech enhancement on listening. The results indicate
that the listeners cannot facilely differentiate between the enhanced speech
signals processed by the baseline model and the proposed SEOFP-NET.
