12,344 research outputs found
Learning Latent Representations for Speech Generation and Transformation
An ability to model a generative process and learn a latent representation
for speech in an unsupervised fashion will be crucial to process vast
quantities of unlabelled speech data. Recently, deep probabilistic generative
models such as Variational Autoencoders (VAEs) have achieved tremendous success
in modeling natural images. In this paper, we apply a convolutional VAE to
model the generative process of natural speech. We derive latent space
arithmetic operations to disentangle learned latent representations. We
demonstrate the capability of our model to modify the phonetic content or the
speaker identity for speech segments using the derived operations, without the
need for parallel supervisory data.Comment: Accepted to Interspeech 201
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
- …