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Found 12 papers, 2 papers with code
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.
PeriodGrad: Towards Pitch-Controllable Neural Vocoder Based on a Diffusion Probabilistic Model
This paper presents a neural vocoder based on a denoising diffusion probabilistic model (DDPM) incorporating explicit periodic signals as auxiliary conditioning signals.
An Integration of 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, including automatic speech recognition (ASR), in an end-to-end (E2E) manner.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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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.
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.
Singing voice synthesis based on frame-level sequence-to-sequence models considering vocal timing deviation
In the proposed system, the attention mechanism absorbs alignment errors in phoneme boundaries.
Singing Voice Synthesis Based on a Musical Note Position-Aware Attention Mechanism
This paper proposes a novel sequence-to-sequence (seq2seq) model with a musical note position-aware attention mechanism for singing voice synthesis (SVS).
Embedding a Differentiable Mel-cepstral Synthesis Filter to a Neural Speech Synthesis System
This paper integrates a classic mel-cepstral synthesis filter into a modern neural speech synthesis system towards end-to-end controllable speech synthesis.
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.
Sinsy: A Deep Neural Network-Based Singing Voice Synthesis System
To better model a singing voice, the proposed system incorporates improved approaches to modeling pitch and vibrato and better training criteria into the acoustic model.
PeriodNet: A non-autoregressive waveform generation model with a structure separating periodic and aperiodic components
We also show that the speech waveforms with a pitch outside of the training data range can be generated with more naturalness.
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.
