Search Results for author: Paavo Alku

Found 11 papers, 2 papers with code

Spectral modification for recognition of children’s speech undermismatched conditions

no code implementations NoDaLiDa 2021 Hemant Kumar Kathania, Sudarsana Reddy Kadiri, Paavo Alku, Mikko Kurimo

The proposed method is used to improve the speech intelligibility to enhance the children’s speech recognition using an acoustic model trained on adult speech.

Automatic Speech Recognition speech-recognition

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).

Glottal Source Processing: from Analysis to Applications

no code implementations29 Dec 2019 Thomas Drugman, Paavo Alku, Abeer Alwan, Bayya Yegnanarayana

The great majority of current voice technology applications relies on acoustic features characterizing the vocal tract response, such as the widely used MFCC of LPC parameters.

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

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