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Found 11 papers, 1 papers with code
PSLM: Parallel Generation of Text and Speech with LLMs for Low-Latency Spoken Dialogue Systems
However, current models face two major challenges in response generation latency: (1) generating a spoken response requires the prior generation of a written response, and (2) speech sequences are significantly longer than text sequences.
Release of Pre-Trained Models for the Japanese Language
AI democratization aims to create a world in which the average person can utilize AI techniques.
Integrating Pre-Trained Speech and Language Models for End-to-End Speech Recognition
Advances in machine learning have made it possible to perform various text and speech processing tasks, such as automatic speech recognition (ASR), in an end-to-end (E2E) manner.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Towards human-like spoken dialogue generation between AI agents from written dialogue
The advent of large language models (LLMs) has made it possible to generate natural written dialogues between two agents.
Focused Prefix Tuning for Controllable Text Generation
In a controllable text generation dataset, there exist unannotated attributes that could provide irrelevant learning signals to models that use it for training and thus degrade their performance.
UniFLG: Unified Facial Landmark Generator from Text or Speech
The two primary frameworks used for talking face generation comprise a text-driven framework, which generates synchronized speech and talking faces from text, and a speech-driven framework, which generates talking faces from speech.
Text-Guided Scene Sketch-to-Photo Synthesis
To this end, we leverage knowledge from recent large-scale pre-trained generative models, resulting in text-guided sketch-to-photo synthesis without the need for reference images.
End-to-End Text-to-Speech Based on Latent Representation of Speaking Styles Using Spontaneous Dialogue
A style encoder that extracts a latent speaking style representation from speech is trained jointly with TTS.
MSR-NV: Neural Vocoder Using Multiple Sampling Rates
In this study, we propose a method to handle multiple sampling rates in a single NV, called the MSR-NV.
Hierarchical Multi-Grained Generative Model for Expressive Speech Synthesis
This framework consists of a multi-grained variational autoencoder, a conditional prior, and a multi-level auto-regressive latent converter to obtain the different time-resolution latent variables and sample the finer-level latent variables from the coarser-level ones by taking into account the input text.
Dance Revolution: Long-Term Dance Generation with Music via Curriculum Learning
In this paper, we formalize the music-conditioned dance generation as a sequence-to-sequence learning problem and devise a novel seq2seq architecture to efficiently process long sequences of music features and capture the fine-grained correspondence between music and dance.
Ranked #1 on
Motion Synthesis
on BRACE
