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Found 13 papers, 0 papers with code
StoryTTS: A Highly Expressive Text-to-Speech Dataset with Rich Textual Expressiveness Annotations
While acoustic expressiveness has long been studied in expressive text-to-speech (ETTS), the inherent expressiveness in text lacks sufficient attention, especially for ETTS of artistic works.
The X-LANCE Technical Report for Interspeech 2024 Speech Processing Using Discrete Speech Unit Challenge
Discrete speech tokens have been more and more popular in multiple speech processing fields, including automatic speech recognition (ASR), text-to-speech (TTS) and singing voice synthesis (SVS).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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VALL-T: Decoder-Only Generative Transducer for Robust and Decoding-Controllable Text-to-Speech
Recent TTS models with decoder-only Transformer architecture, such as SPEAR-TTS and VALL-E, achieve impressive naturalness and demonstrate the ability for zero-shot adaptation given a speech prompt.
SEF-VC: Speaker Embedding Free Zero-Shot Voice Conversion with Cross Attention
Zero-shot voice conversion (VC) aims to transfer the source speaker timbre to arbitrary unseen target speaker timbre, while keeping the linguistic content unchanged.
Expressive TTS Driven by Natural Language Prompts Using Few Human Annotations
The LLM selects the best-matching style references from annotated utterances based on external style prompts, which can be raw input text or natural language style descriptions.
Leveraging Speech PTM, Text LLM, and Emotional TTS for Speech Emotion Recognition
In this paper, we explored how to boost speech emotion recognition (SER) with the state-of-the-art speech pre-trained model (PTM), data2vec, text generation technique, GPT-4, and speech synthesis technique, Azure TTS.
VoiceFlow: Efficient Text-to-Speech with Rectified Flow Matching
Although diffusion models in text-to-speech have become a popular choice due to their strong generative ability, the intrinsic complexity of sampling from diffusion models harms their efficiency.
DSE-TTS: Dual Speaker Embedding for Cross-Lingual Text-to-Speech
Although high-fidelity speech can be obtained for intralingual speech synthesis, cross-lingual text-to-speech (CTTS) is still far from satisfactory as it is difficult to accurately retain the speaker timbres(i. e. speaker similarity) and eliminate the accents from their first language(i. e. nativeness).
DiffVoice: Text-to-Speech with Latent Diffusion
In this work, we present DiffVoice, a novel text-to-speech model based on latent diffusion.
EmoDiff: Intensity Controllable Emotional Text-to-Speech with Soft-Label Guidance
Specifically, instead of being guided with a one-hot vector for the specified emotion, EmoDiff is guided with a soft label where the value of the specified emotion and \textit{Neutral} is set to $\alpha$ and $1-\alpha$ respectively.
VQTTS: High-Fidelity Text-to-Speech Synthesis with Self-Supervised VQ Acoustic Feature
The mainstream neural text-to-speech(TTS) pipeline is a cascade system, including an acoustic model(AM) that predicts acoustic feature from the input transcript and a vocoder that generates waveform according to the given acoustic feature.
Unsupervised word-level prosody tagging for controllable speech synthesis
Although word-level prosody modeling in neural text-to-speech (TTS) has been investigated in recent research for diverse speech synthesis, it is still challenging to control speech synthesis manually without a specific reference.
GlobalWalk: Learning Global-aware Node Embeddings via Biased Sampling
Popular node embedding methods such as DeepWalk follow the paradigm of performing random walks on the graph, and then requiring each node to be proximate to those appearing along with it.
