23 citations · 26 across the 9 of their papers we have counts for
9 papers
BASE TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K hours of data
Mateusz Łajszczak, Guillermo Cámbara, Yang Li +16
We introduce a text-to-speech (TTS) model called BASE TTS, which stands for ig daptive treamable TTS with mergent abilities. BASE TT…
Enhancing the Stability of LLM-based Speech Generation Systems through Self-Supervised Representations
Álvaro Martín-Cortinas, Daniel Sáez-Trigueros, Iván Vallés-Pérez +6
Large Language Models (LLMs) are one of the most promising technologies for the next era of speech generation systems, due to their scalability and in-context learning capabilities…
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…
Simple and Effective Multi-sentence TTS with Expressive and Coherent Prosody
Peter Makarov, Ammar Abbas, Mateusz Łajszczak +5
Generating expressive and contextually appropriate prosody remains a challenge for modern text-to-speech (TTS) systems. This is particularly evident for long, multi-sentence inputs…
CopyCat2: A Single Model for Multi-Speaker TTS and Many-to-Many Fine-Grained Prosody Transfer
Sri Karlapati, Penny Karanasou, Mateusz Lajszczak +7
In this paper, we present CopyCat2 (CC2), a novel model capable of: a) synthesizing speech with different speaker identities, b) generating speech with expressive and contextually…
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…
Interpretable Deep Learning Model for the Detection and Reconstruction of Dysarthric Speech
Daniel Korzekwa, Roberto Barra-Chicote, Bozena Kostek +2
This paper proposed a novel approach for the detection and reconstruction of dysarthric speech. The encoder-decoder model factorizes speech into a low-dimensional latent space and…
In Other News: A Bi-style Text-to-speech Model for Synthesizing Newscaster Voice with Limited Data
Nishant Prateek, Mateusz Łajszczak, Roberto Barra-Chicote +5
Neural text-to-speech synthesis (NTTS) models have shown significant progress in generating high-quality speech, however they require a large quantity of training data. This makes…