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Found 16 papers, 0 papers with code
AE-Flow: AutoEncoder Normalizing Flow
The results show that the proposed training paradigm systematically improves speaker similarity and naturalness when compared to regular training methods of normalizing flows.
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.
On granularity of prosodic representations in expressive text-to-speech
In expressive speech synthesis it is widely adopted to use latent prosody representations to deal with variability of the data during training.
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.
Automated detection of pronunciation errors in non-native English speech employing deep learning
One of the problems with existing CAPT methods is the low availability of annotated mispronounced speech needed for reliable training of pronunciation error detection models.
Computer-assisted Pronunciation Training -- Speech synthesis is almost all you need
We show that these techniques not only improve the accuracy of three machine learning models for detecting pronunciation errors but also help establish a new state-of-the-art in the field.
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.
Enhancing audio quality for expressive Neural Text-to-Speech
Artificial speech synthesis has made a great leap in terms of naturalness as recent Text-to-Speech (TTS) systems are capable of producing speech with similar quality to human recordings.
Non-Autoregressive TTS with Explicit Duration Modelling for Low-Resource Highly Expressive Speech
In this paper, we present a method for building highly expressive TTS voices with as little as 15 minutes of speech data from the target speaker.
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 $60\%$.
Weakly-supervised word-level pronunciation error detection in non-native English speech
To train this model, phonetically transcribed L2 speech is not required and we only need to mark mispronounced words.
Universal Neural Vocoding with Parallel WaveNet
We present a universal neural vocoder based on Parallel WaveNet, with an additional conditioning network called Audio Encoder.
Mispronunciation Detection in Non-native (L2) English with Uncertainty Modeling
A common approach to the automatic detection of mispronunciation in language learning is to recognize the phonemes produced by a student and compare it to the expected pronunciation of a native speaker.
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).
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.
