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Found 10 papers, 1 papers with code
Comparing normalizing flows and diffusion models for prosody and acoustic modelling in text-to-speech
Neural text-to-speech systems are often optimized on L1/L2 losses, which make strong assumptions about the distributions of the target data space.
Creating Personalized Synthetic Voices from Post-Glossectomy Speech with Guided Diffusion Models
Specifically, a diffusion-based speech synthesis model is trained on original recordings, to capture and preserve the target speaker's original articulation style.
iEmoTTS: Toward Robust Cross-Speaker Emotion Transfer and Control for Speech Synthesis based on Disentanglement between Prosody and Timbre
The emotion encoder extracts the identity of emotion type as well as the respective emotion intensity from the mel-spectrogram of input speech.
Mixed-Phoneme BERT: Improving BERT with Mixed Phoneme and Sup-Phoneme Representations for Text to Speech
However, the works apply pre-training with character-based units to enhance the TTS phoneme encoder, which is inconsistent with the TTS fine-tuning that takes phonemes as input.
A study on the efficacy of model pre-training in developing neural text-to-speech system
However, in terms of ultimately achieved system performance for target speaker(s), the actual benefits of model pre-training are uncertain and unstable, depending very much on the quantity and text content of training data.
Environment Aware Text-to-Speech Synthesis
The key idea is to model the acoustic environment in speech audio as a factor of data variability and incorporate it as a condition in the process of neural network based speech synthesis.
Applying the Information Bottleneck Principle to Prosodic Representation Learning
This paper describes a novel design of a neural network-based speech generation model for learning prosodic representation. The problem of representation learning is formulated according to the information bottleneck (IB) principle.
AdaSpeech 3: Adaptive Text to Speech for Spontaneous Style
While recent text to speech (TTS) models perform very well in synthesizing reading-style (e. g., audiobook) speech, it is still challenging to synthesize spontaneous-style speech (e. g., podcast or conversation), mainly because of two reasons: 1) the lack of training data for spontaneous speech; 2) the difficulty in modeling the filled pauses (um and uh) and diverse rhythms in spontaneous speech.
CUHK-EE Voice Cloning System for ICASSP 2021 M2VoC Challenge
100 and 5 utterances of 3 target speakers in different voice and style are provided in track 1 and 2 respectively, and the participants are required to synthesize speech in target speaker's voice and style.
ItLnc-BXE: a Bagging-XGBoost-ensemble method with multiple features for identification of plant lncRNAs
However, most of the existing methods are designed for lncRNAs in animal systems, and only a few methods focus on the plant lncRNA identification.
