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Found 18 papers, 11 papers with code
AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
On the other hand, CVAE training is simple but does not come with the distribution-matching property of a GAN.
Unsupervised Speech Decomposition via Triple Information Bottleneck
Speech information can be roughly decomposed into four components: language content, timbre, pitch, and rhythm.
ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers
Self-supervised learning in speech involves training a speech representation network on a large-scale unannotated speech corpus, and then applying the learned representations to downstream tasks.
Global Rhythm Style Transfer Without Text Transcriptions
In this paper, we propose AutoPST, which can disentangle global prosody style from speech without relying on any text transcriptions.
SpeechSplit 2.0: Unsupervised speech disentanglement for voice conversion Without tuning autoencoder Bottlenecks
SpeechSplit can perform aspect-specific voice conversion by disentangling speech into content, rhythm, pitch, and timbre using multiple autoencoders in an unsupervised manner.
Unsupervised Text-to-Speech Synthesis by Unsupervised Automatic Speech Recognition
An unsupervised text-to-speech synthesis (TTS) system learns to generate speech waveforms corresponding to any written sentence in a language by observing: 1) a collection of untranscribed speech waveforms in that language; 2) a collection of texts written in that language without access to any transcribed speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
WAVPROMPT: Towards Few-Shot Spoken Language Understanding with Frozen Language Models
We show that WavPrompt is a few-shot learner that can perform speech understanding tasks better than a naive text baseline.
F0-consistent many-to-many non-parallel voice conversion via conditional autoencoder
Recently, AutoVC, a conditional autoencoders (CAEs) based method achieved state-of-the-art results by disentangling the speaker identity and speech content using information-constraining bottlenecks, and it achieves zero-shot conversion by swapping in a different speaker's identity embedding to synthesize a new voice.
Speech Denoising with Auditory Models
Contemporary speech enhancement predominantly relies on audio transforms that are trained to reconstruct a clean speech waveform.
Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing
We believe S$^3$-Router has provided a new perspective for practical deployment of speech SSL models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling
Uncertainty decomposition refers to the task of decomposing the total uncertainty of a model into data (aleatoric) uncertainty, resulting from the inherent complexity or ambiguity of the data, and model (epistemic) uncertainty, resulting from the lack of knowledge in the model.
Deep Learning Based Speech Beamforming
On the other hand, deep learning based enhancement approaches are able to learn complicated speech distributions and perform efficient inference, but they are unable to deal with variable number of input channels.
An Efficient and Margin-Approaching Zero-Confidence Adversarial Attack
The second paradigm, called the zero-confidence attack, finds the smallest perturbation needed to cause mis-classification, also known as the margin of an input feature.
PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition
We investigate the existence of sparse subnetworks in pre-trained speech SSL models that achieve even better low-resource ASR results.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
On the Interplay Between Sparsity, Naturalness, Intelligibility, and Prosody in Speech Synthesis
Are end-to-end text-to-speech (TTS) models over-parametrized?
Continuous Convolutional Neural Network forNonuniform Time Series
Convolutional neural network (CNN) for time series data implicitly assumes that the data are uniformly sampled, whereas many event-based and multi-modal data are nonuniform or have heterogeneous sampling rates.
Physics-Driven Diffusion Models for Impact Sound Synthesis from Videos
Traditional methods of impact sound synthesis use physics simulation to obtain a set of physics parameters that could represent and synthesize the sound.
Master-ASR: Achieving Multilingual Scalability and Low-Resource Adaptation in ASR with Modular Learning
Despite the impressive performance recently achieved by automatic speech recognition (ASR), we observe two primary challenges that hinder its broader applications: (1) The difficulty of introducing scalability into the model to support more languages with limited training, inference, and storage overhead; (2) The low-resource adaptation ability that enables effective low-resource adaptation while avoiding over-fitting and catastrophic forgetting issues.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
