Search Results for author:
Found 9 papers, 3 papers with code
Improving Audio Codec-based Zero-Shot Text-to-Speech Synthesis with Multi-Modal Context and Large Language Model
Recent advances in large language models (LLMs) and development of audio codecs greatly propel the zero-shot TTS.
FMPAF: How Do Fed Chairs Affect the Financial Market? A Fine-grained Monetary Policy Analysis Framework on Their Language
The effectiveness of central bank communication is a crucial aspect of monetary policy transmission.
Auffusion: Leveraging the Power of Diffusion and Large Language Models for Text-to-Audio Generation
Drawing inspiration from state-of-the-art Text-to-Image (T2I) diffusion models, we introduce Auffusion, a TTA system adapting T2I model frameworks to TTA task, by effectively leveraging their inherent generative strengths and precise cross-modal alignment.
Ranked #6 on
Audio Generation
on AudioCaps
Frame-level emotional state alignment method for speech emotion recognition
To address this problem, we propose a frame-level emotional state alignment method for SER.
CONCSS: Contrastive-based Context Comprehension for Dialogue-appropriate Prosody in Conversational Speech Synthesis
In this paper, we introduce a contrastive learning-based CSS framework, CONCSS.
Rhythm-controllable Attention with High Robustness for Long Sentence Speech Synthesis
Regressive Text-to-Speech (TTS) system utilizes attention mechanism to generate alignment between text and acoustic feature sequence.
M2-CTTS: End-to-End Multi-scale Multi-modal Conversational Text-to-Speech Synthesis
However, it is still a challenge to comprehensively model the conversation, and a majority of conversational TTS systems only focus on extracting global information and omit local prosody features, which contain important fine-grained information like keywords and emphasis.
ECAPA-TDNN for Multi-speaker Text-to-speech Synthesis
In recent years, neural network based methods for multi-speaker text-to-speech synthesis (TTS) have made significant progress.
