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Found 11 papers, 5 papers with code
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.
Universal Adaptor: Converting Mel-Spectrograms Between Different Configurations for Speech Synthesis
Most recent speech synthesis systems are composed of a synthesizer and a vocoder.
Meta-TTS: Meta-Learning for Few-Shot Speaker Adaptive Text-to-Speech
On the one hand, speaker adaptation methods fine-tune a trained multi-speaker text-to-speech (TTS) model with few enrolled samples.
Analyzing the Robustness of Unsupervised Speech Recognition
In this work, we further analyze the training robustness of unsupervised ASR on the domain mismatch scenarios in which the domains of unpaired speech and text are different.
SpeechNet: A Universal Modularized Model for Speech Processing Tasks
The multi-task learning of a wide variety of speech processing tasks with a universal model has not been studied.
SUPERB: Speech processing Universal PERformance Benchmark
SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data.
Stabilizing Label Assignment for Speech Separation by Self-supervised Pre-training
Speech separation has been well developed, with the very successful permutation invariant training (PIT) approach, although the frequent label assignment switching happening during PIT training remains to be a problem when better convergence speed and achievable performance are desired.
Ranked #6 on
Speech Separation
on Libri2Mix
(using extra training data)
Contextualizing ASR Lattice Rescoring with Hybrid Pointer Network Language Model
Videos uploaded on social media are often accompanied with textual descriptions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Completely Unsupervised Speech Recognition By A Generative Adversarial Network Harmonized With Iteratively Refined Hidden Markov Models
Producing a large annotated speech corpus for training ASR systems remains difficult for more than 95% of languages all over the world which are low-resourced, but collecting a relatively big unlabeled data set for such languages is more achievable.
Completely Unsupervised Phoneme Recognition by Adversarially Learning Mapping Relationships from Audio Embeddings
Unsupervised discovery of acoustic tokens from audio corpora without annotation and learning vector representations for these tokens have been widely studied.
Attention-based Memory Selection Recurrent Network for Language Modeling
Recurrent neural networks (RNNs) have achieved great success in language modeling.
