Search Results for author:
Found 24 papers, 3 papers with code
Enhancing the Stability of LLM-based Speech Generation Systems through Self-Supervised Representations
Using speaker-disentangled codes to train LLMs for text-to-speech (TTS) allows the LLM to generate the content and the style of the speech only from the text, similarly to humans, while the speaker identity is provided by the decoder of the VC model.
Creating New Voices using Normalizing Flows
As there is growing interest in synthesizing voices of new speakers, here we investigate the ability of normalizing flows in text-to-speech (TTS) and voice conversion (VC) modes to extrapolate from speakers observed during training to create unseen speaker identities.
Comparing normalizing flows and diffusion models for prosody and acoustic modelling in text-to-speech
Neural text-to-speech systems are often optimized on L1/L2 losses, which make strong assumptions about the distributions of the target data space.
SCRAPS: Speech Contrastive Representations of Acoustic and Phonetic Spaces
We train a CLIP-based model with the aim to learn shared representations of phonetic and acoustic spaces.
Remap, warp and attend: Non-parallel many-to-many accent conversion with Normalizing Flows
Regional accents of the same language affect not only how words are pronounced (i. e., phonetic content), but also impact prosodic aspects of speech such as speaking rate and intonation.
Stutter-TTS: Controlled Synthesis and Improved Recognition of Stuttered Speech
By fine-tuning an ASR model on synthetic stuttered speech we are able to reduce word error by 5. 7% relative on stuttered utterances, with only minor (<0. 2% relative) degradation for fluent utterances.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
GlowVC: Mel-spectrogram space disentangling model for language-independent text-free voice conversion
We evaluate our models in terms of intelligibility, speaker similarity and naturalness for intra- and cross-lingual conversion in seen and unseen languages.
Prosodic Alignment for off-screen automatic dubbing
The goal of automatic dubbing is to perform speech-to-speech translation while achieving audiovisual coherence.
Text-free non-parallel many-to-many voice conversion using normalising flows
We investigate normalising flows for VC in both text-conditioned and text-free scenarios.
Voice Filter: Few-shot text-to-speech speaker adaptation using voice conversion as a post-processing module
It uses voice conversion (VC) as a post-processing module appended to a pre-existing high-quality TTS system and marks a conceptual shift in the existing TTS paradigm, framing the few-shot TTS problem as a VC task.
Machine Translation Verbosity Control for Automatic Dubbing
Automatic dubbing aims at seamlessly replacing the speech in a video document with synthetic speech in a different language.
Improving the expressiveness of neural vocoding with non-affine Normalizing Flows
In the waveform reconstruction task, the proposed model closes the naturalness and signal quality gap from the original PW to recordings by $10\%$, and from other state-of-the-art neural vocoding systems by more than $60\%$.
SynthASR: Unlocking Synthetic Data for Speech Recognition
Our observations show that SynthASR holds great promise in training the state-of-the-art large-scale E2E ASR models for new applications while reducing the costs and dependency on production data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Improving multi-speaker TTS prosody variance with a residual encoder and normalizing flows
This paper proposes a new neural text-to-speech model that approaches the disentanglement problem by conditioning a Tacotron2-like architecture on flow-normalized speaker embeddings, and by substituting the reference encoder with a new learned latent distribution responsible for modeling the intra-sentence variability due to the prosody.
Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention
This paper describes two novel complementary techniques that improve the detection of lexical stress errors in non-native (L2) English speech: attention-based feature extraction and data augmentation based on Neural Text-To-Speech (TTS).
Parallel WaveNet conditioned on VAE latent vectors
In this paper we investigate the use of a sentence-level conditioning vector to improve the signal quality of a Parallel WaveNet neural vocoder.
BOFFIN TTS: Few-Shot Speaker Adaptation by Bayesian Optimization
We present BOFFIN TTS (Bayesian Optimization For FIne-tuning Neural Text To Speech), a novel approach for few-shot speaker adaptation.
From Speech-to-Speech Translation to Automatic Dubbing
We present enhancements to a speech-to-speech translation pipeline in order to perform automatic dubbing.
Using VAEs and Normalizing Flows for One-shot Text-To-Speech Synthesis of Expressive Speech
We propose a Text-to-Speech method to create an unseen expressive style using one utterance of expressive speech of around one second.
Interpretable Deep Learning Model for the Detection and Reconstruction of Dysarthric Speech
This paper proposed a novel approach for the detection and reconstruction of dysarthric speech.
Towards achieving robust universal neural vocoding
This vocoder is shown to be capable of generating speech of consistently good quality (98% relative mean MUSHRA when compared to natural speech) regardless of whether the input spectrogram comes from a speaker or style seen during training or from an out-of-domain scenario when the recording conditions are studio-quality.
In Other News: A Bi-style Text-to-speech Model for Synthesizing Newscaster Voice with Limited Data
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.
Robust universal neural vocoding
This paper introduces a robust universal neural vocoder trained with 74 speakers (comprised of both genders) coming from 17 languages.
Continuous Expressive Speaking Styles Synthesis based on CVSM and MR-HMM
This paper introduces a continuous system capable of automatically producing the most adequate speaking style to synthesize a desired target text.
