In this thesis we examine the autonomous oscillator model for synthesis of speech signals. The contributions comprise an analysis of realizations and training methods for the nonlinear function used in the oscillator model, the combination of the oscillator model with inverse filtering, both significantly increasing the number of `successfully' re-synthesized speech signals, and the introduction of a new technique suitable for the re-generation of the noise-like signal component in speech signals. Nonlinear function models are compared in a one-dimensional modeling task regarding their presupposition for adequate re-synthesis of speech signals, in particular considering stability. The considerations also comprise the structure of the nonlinear functions, with the aspect of the possible interpolation between models for different speech sounds. Both regarding stability of the oscillator and the premiss of a nonlinear function structure that may be pre-defined, RBF networks are found a ...

Nowadays, speech synthesis is part of various daily life applications. The ultimate goal of such technologies consists in extending the possibilities of interaction with the machine, in order to get closer to human-like communications. However, current state-of-the-art systems often lack of realism: although high-quality speech synthesis can be produced by many researchers and companies around the world, synthetic voices are generally perceived as hyperarticulated. In any case, their degree of articulation is fixed once and for all. The present thesis falls within the more general quest for enriching expressivity in speech synthesis. The main idea consists in improving statistical parametric speech synthesis, whose most famous example is Hidden Markov Model (HMM) based speech synthesis, by introducing a control of the articulation degree, so as to enable synthesizers to automatically adapt their way of speaking to the contextual situation, like humans ...

Despite the outstanding progress made on automatic speech recognition (ASR) throughout the last decades, noise-robust ASR still poses a challenge. Tackling with acoustic noise in ASR systems is more important than ever before for a twofold reason: 1) ASR technology has begun to be extensively integrated in intelligent mobile devices (IMDs) such as smartphones to easily accomplish different tasks (e.g. search-by-voice), and 2) IMDs can be used anywhere at any time, that is, under many different acoustic (noisy) conditions. On the other hand, with the aim of enhancing noisy speech, IMDs have begun to embed small microphone arrays, i.e. microphone arrays comprised of a few sensors close each other. These multi-sensor IMDs often embed one microphone (usually at their rear) intended to capture the acoustic environment more than the speaker’s voice. This is the so-called secondary microphone. While classical microphone ...

Parametric modelling of speech signals finds its use in various speech processing applications. Recently, publications concerning sinusoidal speech modelling have been increasingly appeared in scientific literature. The thesis is mainly devoted to the sinusoidal model with harmonically related component sine waves, i.e. the harmonic model. The main objective is to find new approaches to synthetic speech quality improvement. A novel method for speech spectrum envelope determination is introduced. This method uses a staircase envelope considering the spectral behaviour in voiced as well as unvoiced speech frames. The staircase envelope is smoothed by weighted moving average. The determined envelope is parametrized using autoregressive (AR) model or cepstral coefficients. It has been shown that the new method is of most importance in high-pitch speakers. Besides, new methods or modifications of known methods can be found in pitch synchronization, AR model order selection ...

Speech, consisting of an auditory and a visual signal, has always been the most important means of communication between humans. It is well known that an optimal conveyance of the message requires that both the auditory and the visual speech signal can be perceived by the receiver. Nowadays people interact countless times with computer systems in every-day situations. Since the ultimate goal is to make this interaction feel completely natural and familiar, the most optimal way to interact with a computer system is by means of speech. Similar to the speech communication between humans, the most appropriate human-machine interaction consists of audiovisual speech signals. In order to allow the computer system to transfer a spoken message towards its users, an audiovisual speech synthesizer is needed to generate novel audiovisual speech signals based on a given text. This dissertation focuses on ...

Humans without larynx need to use a substitution voice to re-obtain speech. The electro-larynx (EL) is a widely used device but is known for its unnatural and monotonic speech quality. Previous research tackled these problems, but until now no significant improvements could be reported. The EL speech system is a complex system including hardware (artificial excitation source or sound transducer) and software (control and generation of the artificial excitation signal). It is not enough to consider one separated problem, but all aspects of the EL speech system need to be taken into account. In this thesis we would like to push forward the boundaries of the conventional EL device towards a new bionic electro-larynx speech system. We formulate two overall scenarios: a closed-loop scenario, where EL speech is excited and simultaneously recorded using an EL speech system, and the artificial ...

From artificial voices in GPS to automatic systems of dictation, from voice-based identity verification to voice pathology detection, speech processing applications are nowadays omnipresent in our daily life. By offering solutions to companies seeking for efficiency enhancement with simultaneous cost saving, the market of speech technology is forecast to be especially promising in the next years. The present thesis deals with advances in glottal analysis in order to incorporate new techniques within speech processing applications. While current systems are usually based on information related to the vocal tract configuration, the airflow passing through the vocal folds, and called glottal flow, is expected to exhibit a relevant complementarity. Unfortunately, glottal analysis from speech recordings requires specific complex processing operations, which explains why it has been generally avoided. The main goal of this thesis is to provide new advances in glottal analysis ...

Cochlear implants (CI) are surgically implanted hearing aids that provide auditory sensations to deaf people through direct electrical stimulation of the auditory nerve. Although relatively good speech understanding can be achieved by implanted subjects, pitch perception by CI subjects is about 50 times worse than observed for normal-hearing (NH) persons. Pitch is, however, important for intonation, music, speech understanding in tonal languages, and for separating multiple simultaneous sound sources. The major goal of this work is to improve pitch perception by CI subjects. In CI subjects two fundamental mechanisms are used for pitch perception: place pitch and temporal pitch. Our results show that place pitch is correlated to the sound¢s brightness because place pitch sensation is related to the centroid of the excitation pattern along the cochlea. The slopes of the excitation pattern determine place pitch sensitivity. Our results also ...

Voice disorders need to be better understood because they may lead to reduced job chances and social isolation. Correct treatment indication and treatment effect measurements are needed to tackle these problems. They must rely on robust outcome measures for clinical intervention studies. Diplophonia is a severe and often misunderstood sign of voice disorders. Depending on its underlying etiology, diplophonic patients typically receive treatment such as logopedic therapy or phonosurgery. In the current clinical practice diplophonia is determined auditively by the medical doctor, which is problematic from the viewpoints of evidence-based medicine and scientific methodology. The aim of this thesis is to work towards objective (i.e., automatic) detection of diplophonia. A database of 40 euphonic, 40 diplophonic and 40 dysphonic subjects has been acquired. The collected material consists of laryngeal high-speed videos and simultaneous high-quality audio recordings. All material has been ...

In the early days of speech synthesis research, understanding voice production has attracted the attention of scientists with the goal of producing intelligible speech. Later, the need to produce more natural voices led researchers to use prerecorded voice databases, containing speech units, reassembled by a concatenation algorithm. With the outgrowth of computer capacities, the length of units increased, going from diphones to non-uniform units, in the so-called unit selection framework, using a strategy referred to as 'take the best, modify the least'. Today the new challenge in voice synthesis is the production of expressive speech or singing. The mainstream solution to this problem is based on the “there is no data like more data” paradigm: emotionspecific databases are recorded and emotion-specific units are segmented. In this thesis, we propose to restart the expressive speech synthesis problem, from its original voice ...

This thesis describes the application of a perceptually based spectral subtraction algorithm for the enhancement of non-stationary noise corrupted speech. Through examination of speech enhancement techniques, explanations are given for the choice of magnitude spectral subtraction and how the human auditory system can be modelled for frequency domain speech enhancement. It is discovered, that the cochlea provides the mechanical speech enhancement in the auditory system, through the use of masking. Frequency masking is used in spectral subtraction, to improve the algorithm execution time, and to shape the enhancement process making it sound natural to the ear. A new technique for estimation of background noise is presented, which operates during speech sections as well as pauses. This uses two microphones placed on opposite ends of the cellular handset. Using these, the algorithm determines whether the signal is speech, or noise, by ...

Smart applications like speaker verification have become essential in verifying the user's identity for availing of personal assistants or online banking services based on the user's voice characteristics. However, far-field or distant speaker verification is constantly affected by surrounding noises which can severely distort the speech signal. Moreover, speech signals propagating in long-range get reflected by various objects in the surrounding area, which creates reverberation and further degrades the signal quality. This PhD thesis explores deep learning-based multichannel speech enhancement techniques to improve the performance of speaker verification systems in real conditions. Multichannel speech enhancement aims to enhance distorted speech using multiple microphones. It has become crucial to many smart devices, which are flexible and convenient for speech applications. Three novel approaches are proposed to improve the robustness of speaker verification systems in noisy and reverberated conditions. Firstly, we integrate ...

It takes more time to think of a silent scene, action or event than finding one that emanates sound. Not only speaking or playing music but almost everything that happens is accompanied with or results in one or more sounds mixed together. This makes acoustic event detection (AED) one of the most researched topics in audio signal processing nowadays and it will probably not see a decline anywhere in the near future. This is due to the thirst for understanding and digitally abstracting more and more events in life via the enormous amount of recorded audio through thousands of applications in our daily routine. But it is also a result of two intrinsic properties of audio: it doesn’t need a direct sight to be perceived and is less intrusive to record when compared to image or video. Many applications such ...

Cross-lingual voice conversion refers to the automatic transformation of a source speaker’s voice to a target speaker’s voice in a language that the target speaker can not speak. It involves a set of statistical analysis, pattern recognition, machine learning, and signal processing techniques. This study focuses on the problems related to cross-lingual voice conversion by discussing open research questions, presenting new methods, and performing comparisons with the state-of-the-art techniques. In the training stage, a Phonetic Hidden Markov Model based automatic segmentation and alignment method is developed for cross-lingual applications which support textindependent and text-dependent modes. Vocal tract transformation function is estimated using weighted speech frame mapping in more detail. Adjusting the weights, similarity to target voice and output quality can be balanced depending on the requirements of the cross- lingual voice conversion application. A context-matching algorithm is developed to reduce ...

The research in this thesis addresses the problem of automatic speech recognition in noisy environments. Automatic speech recognition systems obtain acceptable performances in noise free conditions but these performances degrade dramatically in presence of additive noise. This is mainly due to the mismatch between the training and the noisy operating conditions. In the time-frequency representation of the noisy speech signal, some of the clean speech features are masked by noise. In this case the clean speech features cannot be correctly estimated from the noisy speech and therefore they are considered as missing or unreliable. In order to improve the performance of speech recognition systems in additive noise conditions, special attention should be paid to the problems of detection and compensation of these unreliable features. This thesis is concerned with the problem of missing features applied to automatic speaker-independent speech recognition. ...