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Found 15 papers, 7 papers with code
Controllable Emphasis with zero data for text-to-speech
We show that this technique significantly closes the gap to methods that require explicit recordings.
Analysis and Utilization of Entrainment on Acoustic and Emotion Features in User-agent Dialogue
Entrainment is the phenomenon by which an interlocutor adapts their speaking style to align with their partner in conversations.
Distribution augmentation for low-resource expressive text-to-speech
This paper presents a novel data augmentation technique for text-to-speech (TTS), that allows to generate new (text, audio) training examples without requiring any additional data.
Discrete Acoustic Space for an Efficient Sampling in Neural Text-To-Speech
We present a Split Vector Quantized Variational Autoencoder (SVQ-VAE) architecture using a split vector quantizer for NTTS, as an enhancement to the well-known Variational Autoencoder (VAE) and Vector Quantized Variational Autoencoder (VQ-VAE) architectures.
Proteno: Text Normalization with Limited Data for Fast Deployment in Text to Speech Systems
Developing Text Normalization (TN) systems for Text-to-Speech (TTS) on new languages is hard.
Prosodic Phrase Alignment for Machine Dubbing
Dubbing is a type of audiovisual translation where dialogues are translated and enacted so that they give the impression that the media is in the target language.
Problem-Agnostic Speech Embeddings for Multi-Speaker Text-to-Speech with SampleRNN
This way we feed the acoustic model with speaker acoustically dependent representations that enrich the waveform generation more than discrete embeddings unrelated to these factors.
Sound Audio and Speech Processing
Towards Generalized Speech Enhancement with Generative Adversarial Networks
The speech enhancement task usually consists of removing additive noise or reverberation that partially mask spoken utterances, affecting their intelligibility.
Learning Problem-agnostic Speech Representations from Multiple Self-supervised Tasks
Learning good representations without supervision is still an open issue in machine learning, and is particularly challenging for speech signals, which are often characterized by long sequences with a complex hierarchical structure.
Ranked #2 on
Distant Speech Recognition
on DIRHA English WSJ
Self-Attention Linguistic-Acoustic Decoder
The conversion from text to speech relies on the accurate mapping from linguistic to acoustic symbol sequences, for which current practice employs recurrent statistical models like recurrent neural networks.
Whispered-to-voiced Alaryngeal Speech Conversion with Generative Adversarial Networks
Most methods of voice restoration for patients suffering from aphonia either produce whispered or monotone speech.
Language and Noise Transfer in Speech Enhancement Generative Adversarial Network
In this work, we present the results of adapting a speech enhancement generative adversarial network by finetuning the generator with small amounts of data.
SEGAN: Speech Enhancement Generative Adversarial Network
In contrast to current techniques, we operate at the waveform level, training the model end-to-end, and incorporate 28 speakers and 40 different noise conditions into the same model, such that model parameters are shared across them.
Building Synthetic Voices in the META-NET Framework
It is a project in the META-NET Network of Excellence, a cluster of projects aiming at fostering the mission of META, which is the Multilingual Europe Technology Alliance, dedicated to building the technological foundations of a multilingual European information society.
BUCEADOR, a multi-language search engine for digital libraries
The paper presents the tool functionality, the architecture, the digital library and provide some information about the technology involved in the fields of automatic speech recognition, statistical machine translation, text-to-speech synthesis and information retrieval.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+7
