Search Results for author:
Found 11 papers, 2 papers with code
Distribution augmentation for low-resource expressive text-to-speech
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.
Formant Tracking Using Quasi-Closed Phase Forward-Backward Linear Prediction Analysis and Deep Neural Networks
Formant tracking is investigated in this study by using trackers based on dynamic programming (DP) and deep neural nets (DNNs).
Multi-Scale Spectrogram Modelling for Neural Text-to-Speech
We propose a novel Multi-Scale Spectrogram (MSS) modelling approach to synthesise speech with an improved coarse and fine-grained prosody.
GELP: GAN-Excited Linear Prediction for Speech Synthesis from Mel-spectrogram
Recent advances in neural network -based text-to-speech have reached human level naturalness in synthetic speech.
Generative adversarial network-based glottal waveform model for statistical parametric speech synthesis
The results show that the newly proposed GANs achieve synthesis quality comparable to that of widely-used DNNs, without using an additive noise component.
Waveform generation for text-to-speech synthesis using pitch-synchronous multi-scale generative adversarial networks
The state-of-the-art in text-to-speech synthesis has recently improved considerably due to novel neural waveform generation methods, such as WaveNet.
Speaking style adaptation in Text-To-Speech synthesis using Sequence-to-sequence models with attention
Moreover, we experiment with a WaveNet vocoder in synthesis of Lombard speech.
Speaker-independent raw waveform model for glottal excitation
Recent speech technology research has seen a growing interest in using WaveNets as statistical vocoders, i. e., generating speech waveforms from acoustic features.
Speech waveform synthesis from MFCC sequences with generative adversarial networks
This paper proposes a method for generating speech from filterbank mel frequency cepstral coefficients (MFCC), which are widely used in speech applications, such as ASR, but are generally considered unusable for speech synthesis.
DNN-based Speech Synthesis for Indian Languages from ASCII text
These methods first convert the ASCII text to a phonetic script, and then learn a Deep Neural Network to synthesize speech from that.
The Tutorbot Corpus --- A Corpus for Studying Tutoring Behaviour in Multiparty Face-to-Face Spoken Dialogue
The corpus is targeted and designed towards the development of a dialogue system platform to explore verbal and nonverbal tutoring strategies in multiparty spoken interactions.
