113 citations · 275 across the 15 of their papers we have counts for
16 papers
Can a Machine Distinguish High and Low Amount of Social Creak in Speech?
Anne-Maria Laukkanen, Sudarsana Reddy Kadiri, Shrikanth Narayanan +1
Objectives: ncreased prevalence of social creak particularly among female speakers has been reported in several studies. The study of social creak has been previously conducted by…
Wav2vec-based Detection and Severity Level Classification of Dysarthria from Speech
Farhad Javanmardi, Saska Tirronen, Manila Kodali +2
Automatic detection and severity level classification of dysarthria directly from acoustic speech signals can be used as a tool in medical diagnosis. In this work, the pre-trained…
Analysis and Detection of Pathological Voice using Glottal Source Features
Sudarsana Reddy Kadiri, Paavo Alku
Automatic detection of voice pathology enables objective assessment and earlier intervention for the diagnosis. This study provides a systematic analysis of glottal source features…
Time-Varying Quasi-Closed-Phase Analysis for Accurate Formant Tracking in Speech Signals
Dhananjaya Gowda, Sudarsana Reddy Kadiri, Brad Story +1
In this paper, we propose a new method for the accurate estimation and tracking of formants in speech signals using time-varying quasi-closed-phase (TVQCP) analysis. Conventional f…
Refining a Deep Learning-based Formant Tracker using Linear Prediction Methods
Paavo Alku, Sudarsana Reddy Kadiri, Dhananjaya Gowda
In this study, formant tracking is investigated by refining the formants tracked by an existing data-driven tracker, DeepFormants, using the formants estimated in a model-driven ma…
Severity Classification of Parkinson's Disease from Speech using Single Frequency Filtering-based Features
Sudarsana Reddy Kadiri, Manila Kodali, Paavo Alku
Developing objective methods for assessing the severity of Parkinson's disease (PD) is crucial for improving the diagnosis and treatment. This study proposes two sets of novel feat…
Investigation of Self-supervised Pre-trained Models for Classification of Voice Quality from Speech and Neck Surface Accelerometer Signals
Sudarsana Reddy Kadiri, Farhad Javanmardi, Paavo Alku
Prior studies in the automatic classification of voice quality have mainly studied the use of the acoustic speech signal as input. Recently, a few studies have been carried out by…
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…
Glottal Source Processing: from Analysis to Applications
Thomas Drugman, Paavo Alku, Abeer Alwan +1
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. Non…
ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech
Xin Wang, Junichi Yamagishi, Massimiliano Todisco +37
Automatic speaker verification (ASV) is one of the most natural and convenient means of biometric person recognition. Unfortunately, just like all other biometric systems, ASV is v…
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…