Search Results for author:
Found 31 papers, 6 papers with code
Improving grapheme-to-phoneme conversion by learning pronunciations from speech recordings
The G2P model is used to train a multilingual phone recognition system, which then decodes speech recordings with a phonetic representation.
Multilingual context-based pronunciation learning for Text-to-Speech
Phonetic information and linguistic knowledge are an essential component of a Text-to-speech (TTS) front-end.
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.
Low-data? No problem: low-resource, language-agnostic conversational text-to-speech via F0-conditioned data augmentation
The availability of data in expressive styles across languages is limited, and recording sessions are costly and time consuming.
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.
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.
Cross-speaker style transfer for text-to-speech using data augmentation
The proposed approach relies on voice conversion to first generate high-quality data from the set of supporting expressive speakers.
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.
Voicy: Zero-Shot Non-Parallel Voice Conversion in Noisy Reverberant Environments
Voice Conversion (VC) is a technique that aims to transform the non-linguistic information of a source utterance to change the perceived identity of the speaker.
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.
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.
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.
EmoCat: Language-agnostic Emotional Voice Conversion
In this work we propose EmoCat, a language-agnostic emotional voice conversion model.
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.
Low-resource expressive text-to-speech using data augmentation
While recent neural text-to-speech (TTS) systems perform remarkably well, they typically require a substantial amount of recordings from the target speaker reading in the desired speaking style.
Voice Conversion for Whispered Speech Synthesis
We present an approach to synthesize whisper by applying a handcrafted signal processing recipe and Voice Conversion (VC) techniques to convert normally phonated speech to whispered speech.
Dynamic Prosody Generation for Speech Synthesis using Linguistics-Driven Acoustic Embedding Selection
Recent advances in Text-to-Speech (TTS) have improved quality and naturalness to near-human capabilities when considering isolated sentences.
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.
Transformation of low-quality device-recorded speech to high-quality speech using improved SEGAN model
Nowadays vast amounts of speech data are recorded from low-quality recorder devices such as smartphones, tablets, laptops, and medium-quality microphones.
Sound Audio and Speech Processing
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.
Effect of data reduction on sequence-to-sequence neural TTS
Recent speech synthesis systems based on sampling from autoregressive neural networks models can generate speech almost undistinguishable from human recordings.
Robust universal neural vocoding
This paper introduces a robust universal neural vocoder trained with 74 speakers (comprised of both genders) coming from 17 languages.
Deep Encoder-Decoder Models for Unsupervised Learning of Controllable Speech Synthesis
Generating versatile and appropriate synthetic speech requires control over the output expression separate from the spoken text.
A Spoofing Benchmark for the 2018 Voice Conversion Challenge: Leveraging from Spoofing Countermeasures for Speech Artifact Assessment
As a supplement to subjective results for the 2018 Voice Conversion Challenge (VCC'18) data, we configure a standard constant-Q cepstral coefficient CM to quantify the extent of processing artifacts.
The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods
We present the Voice Conversion Challenge 2018, designed as a follow up to the 2016 edition with the aim of providing a common framework for evaluating and comparing different state-of-the-art voice conversion (VC) systems.
A comparison of recent waveform generation and acoustic modeling methods for neural-network-based speech synthesis
Recent advances in speech synthesis suggest that limitations such as the lossy nature of the amplitude spectrum with minimum phase approximation and the over-smoothing effect in acoustic modeling can be overcome by using advanced machine learning approaches.
High-quality nonparallel voice conversion based on cycle-consistent adversarial network
Although voice conversion (VC) algorithms have achieved remarkable success along with the development of machine learning, superior performance is still difficult to achieve when using nonparallel data.
Can we steal your vocal identity from the Internet?: Initial investigation of cloning Obama's voice using GAN, WaveNet and low-quality found data
Thanks to the growing availability of spoofing databases and rapid advances in using them, systems for detecting voice spoofing attacks are becoming more and more capable, and error rates close to zero are being reached for the ASVspoof2015 database.
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.
