11,752 research outputs found
Simulating dysarthric speech for training data augmentation in clinical speech applications
Training machine learning algorithms for speech applications requires large,
labeled training data sets. This is problematic for clinical applications where
obtaining such data is prohibitively expensive because of privacy concerns or
lack of access. As a result, clinical speech applications are typically
developed using small data sets with only tens of speakers. In this paper, we
propose a method for simulating training data for clinical applications by
transforming healthy speech to dysarthric speech using adversarial training. We
evaluate the efficacy of our approach using both objective and subjective
criteria. We present the transformed samples to five experienced
speech-language pathologists (SLPs) and ask them to identify the samples as
healthy or dysarthric. The results reveal that the SLPs identify the
transformed speech as dysarthric 65% of the time. In a pilot classification
experiment, we show that by using the simulated speech samples to balance an
existing dataset, the classification accuracy improves by about 10% after data
augmentation.Comment: Will appear in Proc. of ICASSP 201
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
Attentive Adversarial Learning for Domain-Invariant Training
Adversarial domain-invariant training (ADIT) proves to be effective in
suppressing the effects of domain variability in acoustic modeling and has led
to improved performance in automatic speech recognition (ASR). In ADIT, an
auxiliary domain classifier takes in equally-weighted deep features from a deep
neural network (DNN) acoustic model and is trained to improve their
domain-invariance by optimizing an adversarial loss function. In this work, we
propose an attentive ADIT (AADIT) in which we advance the domain classifier
with an attention mechanism to automatically weight the input deep features
according to their importance in domain classification. With this attentive
re-weighting, AADIT can focus on the domain normalization of phonetic
components that are more susceptible to domain variability and generates deep
features with improved domain-invariance and senone-discriminativity over ADIT.
Most importantly, the attention block serves only as an external component to
the DNN acoustic model and is not involved in ASR, so AADIT can be used to
improve the acoustic modeling with any DNN architectures. More generally, the
same methodology can improve any adversarial learning system with an auxiliary
discriminator. Evaluated on CHiME-3 dataset, the AADIT achieves 13.6% and 9.3%
relative WER improvements, respectively, over a multi-conditional model and a
strong ADIT baseline.Comment: 5 pages, 1 figure, ICASSP 201
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