Search Results for author: Bajibabu Bollepalli

Found 11 papers, 2 papers with code

Distribution augmentation for low-resource expressive text-to-speech

no code implementations13 Feb 2022 Mateusz Lajszczak, Animesh Prasad, Arent van Korlaar, Bajibabu Bollepalli, Antonio Bonafonte, Arnaud Joly, Marco Nicolis, Alexis Moinet, Thomas Drugman, Trevor Wood, Elena Sokolova

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.

Data Augmentation

Formant Tracking Using Quasi-Closed Phase Forward-Backward Linear Prediction Analysis and Deep Neural Networks

no code implementations5 Jan 2022 Dhananjaya Gowda, Bajibabu Bollepalli, Sudarsana Reddy Kadiri, Paavo Alku

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

no code implementations29 Jun 2021 Ammar Abbas, Bajibabu Bollepalli, Alexis Moinet, Arnaud Joly, Penny Karanasou, Peter Makarov, Simon Slangens, Sri Karlapati, Thomas Drugman

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

1 code implementation8 Apr 2019 Lauri Juvela, Bajibabu Bollepalli, Junichi Yamagishi, Paavo Alku

Recent advances in neural network -based text-to-speech have reached human level naturalness in synthetic speech.

Speech Synthesis

Generative adversarial network-based glottal waveform model for statistical parametric speech synthesis

no code implementations14 Mar 2019 Bajibabu Bollepalli, Lauri Juvela, Paavo Alku

The results show that the newly proposed GANs achieve synthesis quality comparable to that of widely-used DNNs, without using an additive noise component.

Speech Synthesis Text-To-Speech Synthesis

Waveform generation for text-to-speech synthesis using pitch-synchronous multi-scale generative adversarial networks

no code implementations30 Oct 2018 Lauri Juvela, Bajibabu Bollepalli, Junichi Yamagishi, Paavo Alku

The state-of-the-art in text-to-speech synthesis has recently improved considerably due to novel neural waveform generation methods, such as WaveNet.

Image Generation Speech Synthesis +1

Speaker-independent raw waveform model for glottal excitation

no code implementations25 Apr 2018 Lauri Juvela, Vassilis Tsiaras, Bajibabu Bollepalli, Manu Airaksinen, Junichi Yamagishi, Paavo Alku

Recent speech technology research has seen a growing interest in using WaveNets as statistical vocoders, i. e., generating speech waveforms from acoustic features.

Speech Synthesis Text-To-Speech Synthesis +1

Speech waveform synthesis from MFCC sequences with generative adversarial networks

1 code implementation3 Apr 2018 Lauri Juvela, Bajibabu Bollepalli, Xin Wang, Hirokazu Kameoka, Manu Airaksinen, Junichi Yamagishi, Paavo Alku

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.

Speech Synthesis

DNN-based Speech Synthesis for Indian Languages from ASCII text

no code implementations18 Aug 2016 Srikanth Ronanki, Siva Reddy, Bajibabu Bollepalli, Simon King

These methods first convert the ASCII text to a phonetic script, and then learn a Deep Neural Network to synthesize speech from that.

Speech Synthesis Text-To-Speech Synthesis

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