52 research outputs found
Pretext Tasks selection for multitask self-supervised speech representation learning
Through solving pretext tasks, self-supervised learning leverages unlabeled
data to extract useful latent representations replacing traditional input
features in the downstream task. In audio/speech signal processing, a wide
range of features where engineered through decades of research efforts. As it
turns out, learning to predict such features (a.k.a pseudo-labels) has proven
to be a particularly relevant pretext task, leading to useful self-supervised
representations which prove to be effective for downstream tasks. However,
methods and common practices for combining such pretext tasks for better
performance on the downstream task have not been explored and understood
properly. In fact, the process relies almost exclusively on a computationally
heavy experimental procedure, which becomes intractable with the increase of
the number of pretext tasks. This paper introduces a method to select a group
of pretext tasks among a set of candidates. The method we propose estimates
calibrated weights for the partial losses corresponding to the considered
pretext tasks during the self-supervised training process. The experiments
conducted on automatic speech recognition, speaker and emotion recognition
validate our approach, as the groups selected and weighted with our method
perform better than classic baselines, thus facilitating the selection and
combination of relevant pseudo-labels for self-supervised representation
learning
Quaternion Denoising Encoder-Decoder for Theme Identification of Telephone Conversations
International audienceIn the last decades, encoder-decoders or autoencoders (AE) have received a great interest from researchers due to their capability to construct robust representations of documents in a low dimensional subspace. Nonetheless, autoencoders reveal little in way of spoken document internal structure by only considering words or topics contained in the document as an isolate basic element, and tend to overfit with small corpus of documents. Therefore, Quaternion Multi-layer Perceptrons (QMLP) have been introduced to capture such internal latent dependencies , whereas denoising autoencoders (DAE) are composed with different stochastic noises to better process small set of documents. This paper presents a novel autoencoder based on both hitherto-proposed DAE (to manage small corpus) and the QMLP (to consider internal latent structures) called "Quater-nion denoising encoder-decoder" (QDAE). Moreover, the paper defines an original angular Gaussian noise adapted to the speci-ficity of hyper-complex algebra. The experiments, conduced on a theme identification task of spoken dialogues from the DE-CODA framework, show that the QDAE obtains the promising gains of 3% and 1.5% compared to the standard real valued de-noising autoencoder and the QMLP respectively
Deep quaternion neural networks for spoken language understanding
International audienceThe availability of open-source software is playing a remarkable role in the popularization of speech recognition and deep learning. Kaldi, for instance, is nowadays an established framework used to develop state-of-the-art speech recognizers. PyTorch is used to build neural networks with the Python language and has recently spawn tremendous interest within the machine learning community thanks to its simplicity and flexibility. The PyTorch-Kaldi project aims to bridge the gap between these popular toolkits, trying to inherit the efficiency of Kaldi and the flexibility of PyTorch. PyTorch-Kaldi is not only a simple interface between these software, but it embeds several useful features for developing modern speech recognizers. For instance, the code is specifically designed to naturally plug-in user-defined acoustic models. As an alternative, users can exploit several pre-implemented neural networks that can be customized using intuitive configuration files. PyTorch-Kaldi supports multiple feature and label streams as well as combinations of neural networks, enabling the use of complex neural architectures. The toolkit is publicly-released along with a rich documentation and is designed to properly work locally or on HPC clusters. Experiments, that are conducted on several datasets and tasks, show that PyTorch-Kaldi can effectively be used to develop modern state-of-the-art speech recognizers
Automatic Data Augmentation for Domain Adapted Fine-Tuning of Self-Supervised Speech Representations
Self-Supervised Learning (SSL) has allowed leveraging large amounts of
unlabeled speech data to improve the performance of speech recognition models
even with small annotated datasets. Despite this, speech SSL representations
may fail while facing an acoustic mismatch between the pretraining and target
datasets. To address this issue, we propose a novel supervised domain
adaptation method, designed for cases exhibiting such a mismatch in acoustic
domains. It consists in applying properly calibrated data augmentations on a
large clean dataset, bringing it closer to the target domain, and using it as
part of an initial fine-tuning stage. Augmentations are automatically selected
through the minimization of a conditional-dependence estimator, based on the
target dataset. The approach is validated during an oracle experiment with
controlled distortions and on two amateur-collected low-resource domains,
reaching better performances compared to the baselines in both cases.Comment: 6 pages,INTERSPEECH 202
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