27 research outputs found
Streaming Active Learning for Regression Problems Using Regression via Classification
One of the challenges in deploying a machine learning model is that the
model's performance degrades as the operating environment changes. To maintain
the performance, streaming active learning is used, in which the model is
retrained by adding a newly annotated sample to the training dataset if the
prediction of the sample is not certain enough. Although many streaming active
learning methods have been proposed for classification, few efforts have been
made for regression problems, which are often handled in the industrial field.
In this paper, we propose to use the regression-via-classification framework
for streaming active learning for regression. Regression-via-classification
transforms regression problems into classification problems so that streaming
active learning methods proposed for classification problems can be applied
directly to regression problems. Experimental validation on four real data sets
shows that the proposed method can perform regression with higher accuracy at
the same annotation cost
Mutual Learning of Single- and Multi-Channel End-to-End Neural Diarization
Due to the high performance of multi-channel speech processing, we can use
the outputs from a multi-channel model as teacher labels when training a
single-channel model with knowledge distillation. To the contrary, it is also
known that single-channel speech data can benefit multi-channel models by
mixing it with multi-channel speech data during training or by using it for
model pretraining. This paper focuses on speaker diarization and proposes to
conduct the above bi-directional knowledge transfer alternately. We first
introduce an end-to-end neural diarization model that can handle both single-
and multi-channel inputs. Using this model, we alternately conduct i) knowledge
distillation from a multi-channel model to a single-channel model and ii)
finetuning from the distilled single-channel model to a multi-channel model.
Experimental results on two-speaker data show that the proposed method mutually
improved single- and multi-channel speaker diarization performances.Comment: Accepted to IEEE SLT 202