50 citations · 62 across the 7 of their papers we have counts for
10 papers
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.…
Formant Tracking Using Quasi-Closed Phase Forward-Backward Linear Prediction Analysis and Deep Neural Networks
Dhananjaya Gowda, Bajibabu Bollepalli, Sudarsana Reddy Kadiri +1
Formant tracking is investigated in this study by using trackers based on dynamic programming (DP) and deep neural nets (DNNs). Using the DP approach, six formant estimation method…
Multi-Scale Spectrogram Modelling for Neural Text-to-Speech
Ammar Abbas, Bajibabu Bollepalli, Alexis Moinet +6
We propose a novel Multi-Scale Spectrogram (MSS) modelling approach to synthesise speech with an improved coarse and fine-grained prosody. We present a generic multi-scale spectrog…
GELP: GAN-Excited Linear Prediction for Speech Synthesis from Mel-spectrogram
Lauri Juvela, Bajibabu Bollepalli, Junichi Yamagishi +1
Recent advances in neural network -based text-to-speech have reached human level naturalness in synthetic speech. The present sequence-to-sequence models can directly map text to m…
Generative adversarial network-based glottal waveform model for statistical parametric speech synthesis
Bajibabu Bollepalli, Lauri Juvela, Paavo Alku
Recent studies have shown that text-to-speech synthesis quality can be improved by using glottal vocoding. This refers to vocoders that parameterize speech into two parts, the glot…
Waveform generation for text-to-speech synthesis using pitch-synchronous multi-scale generative adversarial networks
Lauri Juvela, Bajibabu Bollepalli, Junichi Yamagishi +1
The state-of-the-art in text-to-speech synthesis has recently improved considerably due to novel neural waveform generation methods, such as WaveNet. However, these methods suffer…
Speaking style adaptation in Text-To-Speech synthesis using Sequence-to-sequence models with attention
Bajibabu Bollepalli, Lauri Juvela, Paavo Alku
Currently, there are increasing interests in text-to-speech (TTS) synthesis to use sequence-to-sequence models with attention. These models are end-to-end meaning that they learn b…
Speaker-independent raw waveform model for glottal excitation
Lauri Juvela, Vassilis Tsiaras, Bajibabu Bollepalli +3
Recent speech technology research has seen a growing interest in using WaveNets as statistical vocoders, i.e., generating speech waveforms from acoustic features. These models have…
Speech waveform synthesis from MFCC sequences with generative adversarial networks
Lauri Juvela, Bajibabu Bollepalli, Xin Wang +4
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 ge…
DNN-based Speech Synthesis for Indian Languages from ASCII text
Srikanth Ronanki, Siva Reddy, Bajibabu Bollepalli +1
Text-to-Speech synthesis in Indian languages has a seen lot of progress over the decade partly due to the annual Blizzard challenges. These systems assume the text to be written in…