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
Self-supervised Fine-tuning for Improved Content Representations by Speaker-invariant Clustering
vectominist/spin
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Self-supervised speech representation models have succeeded in various tasks, but improving them for content-related problems using unlabeled data is challenging.
Bootstrapping meaning through listening: Unsupervised learning of spoken sentence embeddings
lingjzhu/spoken_sent_embedding
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Inducing semantic representations directly from speech signals is a highly challenging task but has many useful applications in speech mining and spoken language understanding.
Variable-rate hierarchical CPC leads to acoustic unit discovery in speech
The success of deep learning comes from its ability to capture the hierarchical structure of data by learning high-level representations defined in terms of low-level ones.
Word Segmentation on Discovered Phone Units with Dynamic Programming and Self-Supervised Scoring
This paper instead revisits an older approach to word segmentation: bottom-up phone-like unit discovery is performed first, and symbolic word segmentation is then performed on top of the discovered units (without influencing the lower level).
Analyzing Speaker Information in Self-Supervised Models to Improve Zero-Resource Speech Processing
bshall/cpc
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In this paper, we first show that the per-utterance mean of CPC features captures speaker information to a large extent.
Unsupervised Acoustic Unit Discovery by Leveraging a Language-Independent Subword Discriminative Feature Representation
In the first stage, a recently proposed method in the task of unsupervised subword modeling is improved by replacing a monolingual out-of-domain (OOD) ASR system with a multilingual one to create a subword-discriminative representation that is more language-independent.
Vector-quantized neural networks for acoustic unit discovery in the ZeroSpeech 2020 challenge
The idea is to learn a representation of speech by predicting future acoustic units.
Bayesian Subspace Hidden Markov Model for Acoustic Unit Discovery
beer-asr/beer
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This work tackles the problem of learning a set of language specific acoustic units from unlabeled speech recordings given a set of labeled recordings from other languages.
Unsupervised speech representation learning using WaveNet autoencoders
We consider the task of unsupervised extraction of meaningful latent representations of speech by applying autoencoding neural networks to speech waveforms.
