Acoustic Unit Discovery
9 papers with code • 1 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Acoustic Unit Discovery
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Latest papers with no code
Revisiting speech segmentation and lexicon learning with better features
We revisit a self-supervised method that segments unlabelled speech into word-like segments.
LUPET: Incorporating Hierarchical Information Path into Multilingual ASR
Many factors have separately shown their effectiveness on improving multilingual ASR.
Regularizing Contrastive Predictive Coding for Speech Applications
These representations significantly reduce the amount of labeled data needed for downstream task performance, such as automatic speech recognition.
Self-supervised language learning from raw audio: Lessons from the Zero Resource Speech Challenge
Recent progress in self-supervised or unsupervised machine learning has opened the possibility of building a full speech processing system from raw audio without using any textual representations or expert labels such as phonemes, dictionaries or parse trees.
Learning Phone Recognition from Unpaired Audio and Phone Sequences Based on Generative Adversarial Network
GAN training is adopted in the first stage to find the mapping relationship between unpaired speech and phone sequence.
A Temporal Extension of Latent Dirichlet Allocation for Unsupervised Acoustic Unit Discovery
In this paper, we present an extension to LDA that uses a Markov chain to model temporal information.
Voice Conversion Based Speaker Normalization for Acoustic Unit Discovery
Discovering speaker independent acoustic units purely from spoken input is known to be a hard problem.
A Hierarchical Subspace Model for Language-Attuned Acoustic Unit Discovery
In the target language, we infer both the language and unit embeddings in an unsupervised manner, and in so doing, we simultaneously learn a subspace of units specific to that language and the units that dwell on it.
Unsupervised acoustic unit discovery for speech synthesis using discrete latent-variable neural networks
For our submission to the ZeroSpeech 2019 challenge, we apply discrete latent-variable neural networks to unlabelled speech and use the discovered units for speech synthesis.
Unsupervised Word Segmentation from Speech with Attention
We present a first attempt to perform attentional word segmentation directly from the speech signal, with the final goal to automatically identify lexical units in a low-resource, unwritten language (UL).
