2 citations · 6 across the 8 of their papers we have counts for
14 papers
Universal Semantic Disentangled Privacy-preserving Speech Representation Learning
Biel Tura Vecino, Subhadeep Maji, Aravind Varier +11
The use of audio recordings of human speech to train LLMs poses privacy concerns due to these models' potential to generate outputs that closely resemble artifacts in the training…
Controllable Emphasis with zero data for text-to-speech
Arnaud Joly, Marco Nicolis, Ekaterina Peterova +11
We present a scalable method to produce high quality emphasis for text-to-speech (TTS) that does not require recordings or annotations. Many TTS models include a phoneme duration m…
Analysis and Utilization of Entrainment on Acoustic and Emotion Features in User-agent Dialogue
Daxin Tan, Nikos Kargas, David McHardy +6
Entrainment is the phenomenon by which an interlocutor adapts their speaking style to align with their partner in conversations. It has been found in different dimensions as acoust…
Distribution augmentation for low-resource expressive text-to-speech
Mateusz Lajszczak, Animesh Prasad, Arent van Korlaar +8
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
Marek Strong, Jonas Rohnke, Antonio Bonafonte +2
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 Autoenco…
Proteno: Text Normalization with Limited Data for Fast Deployment in Text to Speech Systems
Shubhi Tyagi, Antonio Bonafonte, Jaime Lorenzo-Trueba +1
Developing Text Normalization (TN) systems for Text-to-Speech (TTS) on new languages is hard. We propose a novel architecture to facilitate it for multiple languages while using da…
Prosodic Phrase Alignment for Machine Dubbing
Alp Öktem, Mireia Farrús, Antonio Bonafonte
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. It requires a car…
Problem-Agnostic Speech Embeddings for Multi-Speaker Text-to-Speech with SampleRNN
David Álvarez, Santiago Pascual, Antonio Bonafonte
Text-to-speech (TTS) acoustic models map linguistic features into an acoustic representation out of which an audible waveform is generated. The latest and most natural TTS systems…
Towards Generalized Speech Enhancement with Generative Adversarial Networks
Santiago Pascual, Joan Serrà, Antonio Bonafonte
The speech enhancement task usually consists of removing additive noise or reverberation that partially mask spoken utterances, affecting their intelligibility. However, little att…
Learning Problem-agnostic Speech Representations from Multiple Self-supervised Tasks
Santiago Pascual, Mirco Ravanelli, Joan Serrà +2
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 l…
Whispered-to-voiced Alaryngeal Speech Conversion with Generative Adversarial Networks
Santiago Pascual, Antonio Bonafonte, Joan Serrà +1
Most methods of voice restoration for patients suffering from aphonia either produce whispered or monotone speech. Apart from intelligibility, this type of speech lacks expressiven…
Self-Attention Linguistic-Acoustic Decoder
Santiago Pascual, Antonio Bonafonte, Joan Serrà
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…
Language and Noise Transfer in Speech Enhancement Generative Adversarial Network
Santiago Pascual, Maruchan Park, Joan Serrà +2
Speech enhancement deep learning systems usually require large amounts of training data to operate in broad conditions or real applications. This makes the adaptability of those sy…
SEGAN: Speech Enhancement Generative Adversarial Network
Santiago Pascual, Antonio Bonafonte, Joan Serrà
Current speech enhancement techniques operate on the spectral domain and/or exploit some higher-level feature. The majority of them tackle a limited number of noise conditions and…