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Found 19 papers, 9 papers with code
Audio Dialogues: Dialogues dataset for audio and music understanding
Existing datasets for audio understanding primarily focus on single-turn interactions (i. e. audio captioning, audio question answering) for describing audio in natural language, thus limiting understanding audio via interactive dialogue.
Audio Flamingo: A Novel Audio Language Model with Few-Shot Learning and Dialogue Abilities
Augmenting large language models (LLMs) to understand audio -- including non-speech sounds and non-verbal speech -- is critically important for diverse real-world applications of LLMs.
Scaling NVIDIA's Multi-speaker Multi-lingual TTS Systems with Zero-Shot TTS to Indic Languages
In Track 3, we utilize P-Flow to perform zero-shot TTS by training on the challenge dataset as well as external datasets.
SelfVC: Voice Conversion With Iterative Refinement using Self Transformations
In this work, instead of explicitly disentangling attributes with loss terms, we present a framework to train a controllable voice conversion model on entangled speech representations derived from self-supervised learning and speaker verification models.
P-Flow: A Fast and Data-Efficient Zero-Shot TTS through Speech Prompting
P-Flow comprises a speech-prompted text encoder for speaker adaptation and a flow matching generative decoder for high-quality and fast speech synthesis.
VANI: Very-lightweight Accent-controllable TTS for Native and Non-native speakers with Identity Preservation
We introduce VANI, a very lightweight multi-lingual accent controllable speech synthesis system.
Multilingual Multiaccented Multispeaker TTS with RADTTS
We work to create a multilingual speech synthesis system which can generate speech with the proper accent while retaining the characteristics of an individual voice.
SPACE: Speech-driven Portrait Animation with Controllable Expression
It uses a multi-stage approach, combining the controllability of facial landmarks with the high-quality synthesis power of a pretrained face generator.
Generative Modeling for Low Dimensional Speech Attributes with Neural Spline Flows
Despite recent advances in generative modeling for text-to-speech synthesis, these models do not yet have the same fine-grained adjustability of pitch-conditioned deterministic models such as FastPitch and FastSpeech2.
One TTS Alignment To Rule Them All
However, these alignments tend to be brittle and often fail to generalize to long utterances and out-of-domain text, leading to missing or repeating words.
RAD-TTS: Parallel Flow-Based TTS with Robust Alignment Learning and Diverse Synthesis
This work introduces a predominantly parallel, end-to-end TTS model based on normalizing flows.
Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis
In this paper we propose Flowtron: an autoregressive flow-based generative network for text-to-speech synthesis with control over speech variation and style transfer.
Ranked #1 on
Text-To-Speech Synthesis
on LJSpeech
(Pleasantness MOS metric, using extra
training data)
Neural ODEs for Image Segmentation with Level Sets
We propose a novel approach for image segmentation that combines Neural Ordinary Differential Equations (NODEs) and the Level Set method.
Mellotron: Multispeaker expressive voice synthesis by conditioning on rhythm, pitch and global style tokens
Mellotron is a multispeaker voice synthesis model based on Tacotron 2 GST that can make a voice emote and sing without emotive or singing training data.
WaveGlow: A Flow-based Generative Network for Speech Synthesis
In this paper we propose WaveGlow: a flow-based network capable of generating high quality speech from mel-spectrograms.
Ranked #8 on
Speech Synthesis
on LibriTTS
TequilaGAN: How to easily identify GAN samples
In this paper we show strategies to easily identify fake samples generated with the Generative Adversarial Network framework.
Attacking Speaker Recognition With Deep Generative Models
In this paper we investigate the ability of generative adversarial networks (GANs) to synthesize spoofing attacks on modern speaker recognition systems.
Character-Based Handwritten Text Transcription with Attention Networks
When the sequence alignment is one-to-one, softmax attention is able to learn a more precise alignment at each step of the decoding, whereas the alignment generated by sigmoid attention is much less precise.
Missing Data Imputation for Supervised Learning
Missing data imputation can help improve the performance of prediction models in situations where missing data hide useful information.
Ranked #1 on
Imputation
on Adult
