Acoustic Unit Discovery

6 papers with code • 1 benchmarks • 0 datasets

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Most implemented papers

Unsupervised speech representation learning using WaveNet autoencoders

swasun/VQ-VAE-Speech 25 Jan 2019

We consider the task of unsupervised extraction of meaningful latent representations of speech by applying autoencoding neural networks to speech waveforms.

Vector-quantized neural networks for acoustic unit discovery in the ZeroSpeech 2020 challenge

bshall/ZeroSpeech 19 May 2020

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 8 Apr 2019

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 Acoustic Unit Discovery by Leveraging a Language-Independent Subword Discriminative Feature Representation

syfengcuhk/mboshi 2 Apr 2021

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.

Analyzing Speaker Information in Self-Supervised Models to Improve Zero-Resource Speech Processing

bshall/cpc 2 Aug 2021

In this paper, we first show that the per-utterance mean of CPC features captures speaker information to a large extent.

Variable-rate hierarchical CPC leads to acoustic unit discovery in speech

chorowski-lab/hcpc 5 Jun 2022

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.