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Found 13 papers, 4 papers with code
Maximizing Data Efficiency for Cross-Lingual TTS Adaptation by Self-Supervised Representation Mixing and Embedding Initialization
This paper presents an effective transfer learning framework for language adaptation in text-to-speech systems, with a focus on achieving language adaptation using minimal labeled and unlabeled data.
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.
Few-Shot Cross-Lingual TTS Using Transferable Phoneme Embedding
This paper studies a transferable phoneme embedding framework that aims to deal with the cross-lingual text-to-speech (TTS) problem under the few-shot setting.
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.
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.
Non-autoregressive Mandarin-English Code-switching Speech Recognition
Mandarin-English code-switching (CS) is frequently used among East and Southeast Asian people.
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)
Pretrained Language Model Embryology: The Birth of ALBERT
These findings suggest that knowledge of a pretrained model varies during pretraining, and having more pretrain steps does not necessarily provide a model with more comprehensive knowledge.
From Semi-supervised to Almost-unsupervised Speech Recognition with Very-low Resource by Jointly Learning Phonetic Structures from Audio and Text Embeddings
However, we note human babies start to learn the language by the sounds (or phonetic structures) of a small number of exemplar words, and "generalize" such knowledge to other words without hearing a large amount of data.
Improved Audio Embeddings by Adjacency-Based Clustering with Applications in Spoken Term Detection
Embedding audio signal segments into vectors with fixed dimensionality is attractive because all following processing will be easier and more efficient, for example modeling, classifying or indexing.
Almost-unsupervised Speech Recognition with Close-to-zero Resource Based on Phonetic Structures Learned from Very Small Unpaired Speech and Text Data
This can be learned by aligning a small number of spoken words and the corresponding text words in the embedding spaces.
Phonetic-and-Semantic Embedding of Spoken Words with Applications in Spoken Content Retrieval
Stage 1 performs phonetic embedding with speaker characteristics disentangled.
Towards Unsupervised Automatic Speech Recognition Trained by Unaligned Speech and Text only
In this work, we propose a framework to achieve unsupervised ASR on a read English speech dataset, where audio and text are unaligned.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
