Ultrasound based Silent Speech Interface using Deep Learning

Silent Speech Interface (SSI) is a technology able to synthesize speech in the absence of any acoustic signal. It can be useful in cases like laryngectomy patients, noisy environments or silent calls. This thesis explores the particular case of SSI using ultrasound images of the tongue as input signals. A 'direct synthesis' approach based on Deep Neural Networks and Mel-generalized cepstral coefficients is proposed. This document is an extension of Csapó et al. "DNN-based Ultrasound-to-Speech Conversion for a Silent Speech Interface". Several deep learning models, such as the basic Feed-forward Neural Networks, Convolutional Neural Networks and Recurrent Neural Networks are presented and discussed. A denoising pre-processing based on a Deep Convolutional Autoencoder has also been studied. A considerable number of experiments using a set of different deep learning architectures and an extensive hyperperameter optimization study have been realized. The different experiments have been testing and rating several objective and subjective quality measures. According to the experiments, an architecture based on a CNN and bidirectional LSTM layers has shown the best results in both objective and subjective terms.Silent Speech Interface (SSI) is a technology able to synthesize speech in the absence of any acoustic signal. It can be useful in cases like laryngectomy patients, noisy environments or silent calls. This thesis explores the particular case of SSI using ultrasound images of the tongue as input signals. A 'direct synthesis' approach based on Deep Neural Networks and Mel-generalized cepstral coefficients is proposed. This document is an extension of Csapó et al. "DNN-based Ultrasound-to-Speech Conversion for a Silent Speech Interface". Several deep learning models, such as the basic Feed-forward Neural Networks, Convolutional Neural Networks and Recurrent Neural Networks are presented and discussed. A denoising pre-processing based on a Deep Convolutional Autoencoder has also been studied. A considerable number of experiments using a set of different deep learning architectures and an extensive hyperperameter optimization study have been realized. The different experiments have been testing and rating several objective and subjective quality measures. According to the experiments, an architecture based on a CNN and bidirectional LSTM layers has shown the best results in both objective and subjective terms.Silent Speech Interface (SSI) és una tecnologia capaç de sintetitzar veu partint únicament de senyals no-acústiques. Pot tenir gran utilitat en casos com pacients de laringectomia, ambients sorollosos o trucades silencioses. Aquesta tèsis explora el cas particular de SSI utilitzant imatges de la llengua captades amb ultrasons com a senyals d'entrada. Es proposa un enfocament de 'síntesis directa' basat en Xarxes Neuronals Profundes i coeficients Mel-generalized cepstral. Aquest document és una extensió del treball de Csapó et al. "DNN-based Ultrasound-to-Speech Conversion for a Silent Speech Interface" . Diversos models de xarxes neuronals són presentats i discutits, com les bàsiques xarxes neuronals directes, xarxes neuronals convolucionals o xarxes neuronals recurrents. També s'ha estudiat un pre-processat reductor de soroll basat en un Autoencoder convolucional profund. S'ha portat a terme un nombre considerable d'experiments utilitzant diverses arquitectures de Deep Learning, així com un extens estudi d'optimització d'hyperparàmetres. Els diferents experiments han estat evaluar i qualificar a partir de diferentes mesures de qualitat objectives i subjectives. Els millors resultats, tant en termes objectius com subjectius, els ha presentat una arquitectura basada en una CNN i capes bidireccionals de LSTMs

Confident Kernel Sparse Coding and Dictionary Learning

In recent years, kernel-based sparse coding (K-SRC) has received particular attention due to its efficient representation of nonlinear data structures in the feature space. Nevertheless, the existing K-SRC methods suffer from the lack of consistency between their training and test optimization frameworks. In this work, we propose a novel confident K-SRC and dictionary learning algorithm (CKSC) which focuses on the discriminative reconstruction of the data based on its representation in the kernel space. CKSC focuses on reconstructing each data sample via weighted contributions which are confident in its corresponding class of data. We employ novel discriminative terms to apply this scheme to both training and test frameworks in our algorithm. This specific design increases the consistency of these optimization frameworks and improves the discriminative performance in the recall phase. In addition, CKSC directly employs the supervised information in its dictionary learning framework to enhance the discriminative structure of the dictionary. For empirical evaluations, we implement our CKSC algorithm on multivariate time-series benchmarks such as DynTex++ and UTKinect. Our claims regarding the superior performance of the proposed algorithm are justified throughout comparing its classification results to the state-of-the-art K-SRC algorithms.Comment: 10 pages, ICDM 2018 conferenc

3D konvolúciós neuronhálón és neurális vokóderen alapuló némabeszéd-interfész

A némabeszéd-interfészek célja beszédjel előállítása valamilyen, az artikulációs szervek mozgását rögzítő felvételből, például a nyelvmozgást tartalmazó ultrahang-videóból. Jelenleg erre a konverzióra a mély neuronhálókat alkalmazó megoldások tűnnek a legígéretesebbnek. Képek felismerésére már régóta alkalmazzák a konvolúciós neuronhálókat, a legjobb eredményt azonban akkor kaphatjuk, ha a videó egyes képkockáit nem külön-külön, hanem sorozatként dolgozzuk fel. Egy lehetséges megoldás erre, ha a képeket feldolgozó konvolúciós háló kimeneteinek sorozatát egy visszacsatolt neuronhálóval egyesítjük. Jelen cikkben viszont egy másik megoldással próbálkozunk, nevesül 3-dimenziós konvolúciós hálókat használunk, ahol a képek két dimenziója mellett az idő képezi a harmadik tengelyt. A 3D konvolúciós hálóknak is egy speciális változatát alkalmazzuk, amely a térbeli és időbeli konvolúciós lépéseket felbontott formában végzi el – ezt a fajta hálózatot sikeresen használták már más videófelismerési feladatokban is. Kísérleteinkben a 3D neuronháló némileg pontosabb eredményeket adott, mint a kombinált konvolúciós+visszacsatolt modell, ami azt mutatja, hogy ez a megközelítés alternatívája lehet a rekurrens hálókra épülő, általában lassabban és nehézkesebben tanítható modelleknek

Registration and statistical analysis of the tongue shape during speech production

This thesis analyzes the human tongue shape during speech production. First, a semi-supervised approach is derived for estimating the tongue shape from volumetric magnetic resonance imaging data of the human vocal tract. Results of this extraction are used to derive parametric tongue models. Next, a framework is presented for registering sparse motion capture data of the tongue by means of such a model. This method allows to generate full three-dimensional animations of the tongue. Finally, a multimodal and statistical text-to-speech system is developed that is able to synthesize audio and synchronized tongue motion from text.Diese Dissertation beschäftigt sich mit der Analyse der menschlichen Zungenform während der Sprachproduktion. Zunächst wird ein semi-überwachtes Verfahren vorgestellt, mit dessen Hilfe sich Zungenformen von volumetrischen Magnetresonanztomographie- Aufnahmen des menschlichen Vokaltrakts schätzen lassen. Die Ergebnisse dieses Extraktionsverfahrens werden genutzt, um ein parametrisches Zungenmodell zu konstruieren. Danach wird eine Methode hergeleitet, die ein solches Modell nutzt, um spärliche Bewegungsaufnahmen der Zunge zu registrieren. Dieser Ansatz erlaubt es, dreidimensionale Animationen der Zunge zu erstellen. Zuletzt wird ein multimodales und statistisches Text-to-Speech-System entwickelt, das in der Lage ist, Audio und die dazu synchrone Zungenbewegung zu synthetisieren.German Research Foundatio

Speech reconstruction from articulatory movement for laryngectomees

Laryngectomees are individuals who have their larynx surgically removed due to the treatment of laryngeal cancer. They lost their ability to vocalize speech but can still articulate after the surgery. As a result, they rely on alternative methods for communication (e.g., alaryngeal speech). However, alaryngeal speech generates unnatural-sounding voice, which discourages their willingness to speak and causes social isolation and even depression. Silent speech interfaces (SSIs) convert non-audio human bio-signals (e.g., tongue and lip movement) to speech, which have the potential to reconstruct speech with natural-sounding voice and even speaker identity. Although the concept of SSI and its feasibility has been demonstrated in the field, SSI development still faces a few major challenges, including small data size, lack of algorithms for laryngectomees, and lack of wearable devices for daily use. A series of studies were conducted to address these challenges. This dissertation contributed to the field from different aspects, including novel algorithms and approaches for articulation-to-speech mapping, new knowledge to improve the design of SSI, and evaluation of newly developed wearable devices.Electrical and Computer Engineerin