8,474 research outputs found
Transfer Learning for Speech and Language Processing
Transfer learning is a vital technique that generalizes models trained for
one setting or task to other settings or tasks. For example in speech
recognition, an acoustic model trained for one language can be used to
recognize speech in another language, with little or no re-training data.
Transfer learning is closely related to multi-task learning (cross-lingual vs.
multilingual), and is traditionally studied in the name of `model adaptation'.
Recent advance in deep learning shows that transfer learning becomes much
easier and more effective with high-level abstract features learned by deep
models, and the `transfer' can be conducted not only between data distributions
and data types, but also between model structures (e.g., shallow nets and deep
nets) or even model types (e.g., Bayesian models and neural models). This
review paper summarizes some recent prominent research towards this direction,
particularly for speech and language processing. We also report some results
from our group and highlight the potential of this very interesting research
field.Comment: 13 pages, APSIPA 201
Adversarial Speaker Adaptation
We propose a novel adversarial speaker adaptation (ASA) scheme, in which
adversarial learning is applied to regularize the distribution of deep hidden
features in a speaker-dependent (SD) deep neural network (DNN) acoustic model
to be close to that of a fixed speaker-independent (SI) DNN acoustic model
during adaptation. An additional discriminator network is introduced to
distinguish the deep features generated by the SD model from those produced by
the SI model. In ASA, with a fixed SI model as the reference, an SD model is
jointly optimized with the discriminator network to minimize the senone
classification loss, and simultaneously to mini-maximize the SI/SD
discrimination loss on the adaptation data. With ASA, a senone-discriminative
deep feature is learned in the SD model with a similar distribution to that of
the SI model. With such a regularized and adapted deep feature, the SD model
can perform improved automatic speech recognition on the target speaker's
speech. Evaluated on the Microsoft short message dictation dataset, ASA
achieves 14.4% and 7.9% relative word error rate improvements for supervised
and unsupervised adaptation, respectively, over an SI model trained from 2600
hours data, with 200 adaptation utterances per speaker.Comment: 5 pages, 2 figures, ICASSP 201
Acoustic model adaptation for ortolan bunting (Emberiza hortulana L.) song-type classification
Automatic systems for vocalization classification often require fairly large amounts of data on which to train models. However, animal vocalization data collection and transcription is a difficult and time-consuming task, so that it is expensive to create large data sets. One natural solution to this problem is the use of acoustic adaptation methods. Such methods, common in human speech recognition systems, create initial models trained on speaker independent data, then use small amounts of adaptation data to build individual-specific models. Since, as in human speech, individual vocal variability is a significant source of variation in bioacoustic data, acoustic model adaptation is naturally suited to classification in this domain as well. To demonstrate and evaluate the effectiveness of this approach, this paper presents the application of maximum likelihood linear regression adaptation to ortolan bunting (Emberiza hortulana L.) song-type classification. Classification accuracies for the adapted system are computed as a function of the amount of adaptation data and compared to caller-independent and caller-dependent systems. The experimental results indicate that given the same amount of data, supervised adaptation significantly outperforms both caller-independent and caller-dependent systems
Conditional Teacher-Student Learning
The teacher-student (T/S) learning has been shown to be effective for a
variety of problems such as domain adaptation and model compression. One
shortcoming of the T/S learning is that a teacher model, not always perfect,
sporadically produces wrong guidance in form of posterior probabilities that
misleads the student model towards a suboptimal performance. To overcome this
problem, we propose a conditional T/S learning scheme, in which a "smart"
student model selectively chooses to learn from either the teacher model or the
ground truth labels conditioned on whether the teacher can correctly predict
the ground truth. Unlike a naive linear combination of the two knowledge
sources, the conditional learning is exclusively engaged with the teacher model
when the teacher model's prediction is correct, and otherwise backs off to the
ground truth. Thus, the student model is able to learn effectively from the
teacher and even potentially surpass the teacher. We examine the proposed
learning scheme on two tasks: domain adaptation on CHiME-3 dataset and speaker
adaptation on Microsoft short message dictation dataset. The proposed method
achieves 9.8% and 12.8% relative word error rate reductions, respectively, over
T/S learning for environment adaptation and speaker-independent model for
speaker adaptation.Comment: 5 pages, 1 figure, ICASSP 201
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