26,991 research outputs found
Deep factorization for speech signal
Various informative factors mixed in speech signals, leading to great
difficulty when decoding any of the factors. An intuitive idea is to factorize
each speech frame into individual informative factors, though it turns out to
be highly difficult. Recently, we found that speaker traits, which were assumed
to be long-term distributional properties, are actually short-time patterns,
and can be learned by a carefully designed deep neural network (DNN). This
discovery motivated a cascade deep factorization (CDF) framework that will be
presented in this paper. The proposed framework infers speech factors in a
sequential way, where factors previously inferred are used as conditional
variables when inferring other factors. We will show that this approach can
effectively factorize speech signals, and using these factors, the original
speech spectrum can be recovered with a high accuracy. This factorization and
reconstruction approach provides potential values for many speech processing
tasks, e.g., speaker recognition and emotion recognition, as will be
demonstrated in the paper.Comment: Accepted by ICASSP 2018. arXiv admin note: substantial text overlap
with arXiv:1706.0177
Towards Speech Emotion Recognition "in the wild" using Aggregated Corpora and Deep Multi-Task Learning
One of the challenges in Speech Emotion Recognition (SER) "in the wild" is
the large mismatch between training and test data (e.g. speakers and tasks). In
order to improve the generalisation capabilities of the emotion models, we
propose to use Multi-Task Learning (MTL) and use gender and naturalness as
auxiliary tasks in deep neural networks. This method was evaluated in
within-corpus and various cross-corpus classification experiments that simulate
conditions "in the wild". In comparison to Single-Task Learning (STL) based
state of the art methods, we found that our MTL method proposed improved
performance significantly. Particularly, models using both gender and
naturalness achieved more gains than those using either gender or naturalness
separately. This benefit was also found in the high-level representations of
the feature space, obtained from our method proposed, where discriminative
emotional clusters could be observed.Comment: Published in the proceedings of INTERSPEECH, Stockholm, September,
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Feature Learning from Spectrograms for Assessment of Personality Traits
Several methods have recently been proposed to analyze speech and
automatically infer the personality of the speaker. These methods often rely on
prosodic and other hand crafted speech processing features extracted with
off-the-shelf toolboxes. To achieve high accuracy, numerous features are
typically extracted using complex and highly parameterized algorithms. In this
paper, a new method based on feature learning and spectrogram analysis is
proposed to simplify the feature extraction process while maintaining a high
level of accuracy. The proposed method learns a dictionary of discriminant
features from patches extracted in the spectrogram representations of training
speech segments. Each speech segment is then encoded using the dictionary, and
the resulting feature set is used to perform classification of personality
traits. Experiments indicate that the proposed method achieves state-of-the-art
results with a significant reduction in complexity when compared to the most
recent reference methods. The number of features, and difficulties linked to
the feature extraction process are greatly reduced as only one type of
descriptors is used, for which the 6 parameters can be tuned automatically. In
contrast, the simplest reference method uses 4 types of descriptors to which 6
functionals are applied, resulting in over 20 parameters to be tuned.Comment: 12 pages, 3 figure
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