Search Results for author:
Found 12 papers, 2 papers with code
ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech
The database contains attacks generated with 32 different algorithms, also crowdsourced, and optimised to varying degrees using new surrogate detection models.
SegINR: Segment-wise Implicit Neural Representation for Sequence Alignment in Neural Text-to-Speech
It leverages an optimal text encoder to extract embeddings, transforming each into a segment of frame-level features using a conditional implicit neural representation (INR).
High Fidelity Text-to-Speech Via Discrete Tokens Using Token Transducer and Group Masked Language Model
We propose a novel two-stage text-to-speech (TTS) framework with two types of discrete tokens, i. e., semantic and acoustic tokens, for high-fidelity speech synthesis.
MakeSinger: A Semi-Supervised Training Method for Data-Efficient Singing Voice Synthesis via Classifier-free Diffusion Guidance
In this paper, we propose MakeSinger, a semi-supervised training method for singing voice synthesis (SVS) via classifier-free diffusion guidance.
Utilizing Neural Transducers for Two-Stage Text-to-Speech via Semantic Token Prediction
We also delve into the inference speed and prosody control capabilities of our approach, highlighting the potential of neural transducers in TTS frameworks.
Efficient Parallel Audio Generation using Group Masked Language Modeling
We present a fast and high-quality codec language model for parallel audio generation.
Transduce and Speak: Neural Transducer for Text-to-Speech with Semantic Token Prediction
We introduce a text-to-speech(TTS) framework based on a neural transducer.
Towards single integrated spoofing-aware speaker verification embeddings
Second, competitive performance should be demonstrated compared to the fusion of automatic speaker verification (ASV) and countermeasure (CM) embeddings, which outperformed single embedding solutions by a large margin in the SASV2022 challenge.
SNAC: Speaker-normalized affine coupling layer in flow-based architecture for zero-shot multi-speaker text-to-speech
Zero-shot multi-speaker text-to-speech (ZSM-TTS) models aim to generate a speech sample with the voice characteristic of an unseen speaker.
Adversarial Speaker-Consistency Learning Using Untranscribed Speech Data for Zero-Shot Multi-Speaker Text-to-Speech
Several recently proposed text-to-speech (TTS) models achieved to generate the speech samples with the human-level quality in the single-speaker and multi-speaker TTS scenarios with a set of pre-defined speakers.
Transfer Learning Framework for Low-Resource Text-to-Speech using a Large-Scale Unlabeled Speech Corpus
The experimental results verify the effectiveness of the proposed method in terms of naturalness, intelligibility, and speaker generalization.
Diff-TTS: A Denoising Diffusion Model for Text-to-Speech
Although neural text-to-speech (TTS) models have attracted a lot of attention and succeeded in generating human-like speech, there is still room for improvements to its naturalness and architectural efficiency.
