Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy. This edition celebrates twenty years of uninterrupted and succesfully research in the field of voice analysis

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

Characterization of Language Cortex Activity During Speech Production and Perception

Millions of people around the world suffer from severe neuromuscular disorders such as spinal cord injury, cerebral palsy, amyotrophic lateral sclerosis (ALS), and others. Many of these individuals cannot perform daily tasks without assistance and depend on caregivers, which adversely impacts their quality of life. A Brain-Computer Interface (BCI) is technology that aims to give these people the ability to interact with their environment and communicate with the outside world. Many recent studies have attempted to decode spoken and imagined speech directly from brain signals toward the development of a natural-speech BCI. However, the current progress has not reached practical application. An approach to improve the performance of this technology is to better understand the underlying speech processes in the brain for further optimization of existing models. In order to extend research in this direction, this thesis aims to characterize and decode the auditory and articulatory features from the motor cortex using the electrocorticogram (ECoG). Consonants were chosen as auditory representations, and both places of articulation and manners of articulation were chosen as articulatory representations. The auditory and articulatory representations were decoded at different time lags with respect to the speech onset to determine optimal temporal decoding parameters. In addition, this work explores the role of the temporal lobe during speech production directly from ECoG signals. A novel decoding model using temporal lobe activity was developed to predict a spectral representation of the speech envelope during speech production. This new knowledge may be used to enhance existing speech-based BCI systems, which will offer a more natural communication modality. In addition, the work contributes to the field of speech neurophysiology by providing a better understanding of speech processes in the brain

Towards an Integrative Information Society: Studies on Individuality in Speech and Sign

The flow of information within modern information society has increased rapidly over the last decade. The major part of this information flow relies on the individual’s abilities to handle text or speech input. For the majority of us it presents no problems, but there are some individuals who would benefit from other means of conveying information, e.g. signed information flow. During the last decades the new results from various disciplines have all suggested towards the common background and processing for sign and speech and this was one of the key issues that I wanted to investigate further in this thesis. The basis of this thesis is firmly within speech research and that is why I wanted to design analogous test batteries for widely used speech perception tests for signers – to find out whether the results for signers would be the same as in speakers’ perception tests. One of the key findings within biology – and more precisely its effects on speech and communication research – is the mirror neuron system. That finding has enabled us to form new theories about evolution of communication, and it all seems to converge on the hypothesis that all communication has a common core within humans. In this thesis speech and sign are discussed as equal and analogical counterparts of communication and all research methods used in speech are modified for sign. Both speech and sign are thus investigated using similar test batteries. Furthermore, both production and perception of speech and sign are studied separately. An additional framework for studying production is given by gesture research using cry sounds. Results of cry sound research are then compared to results from children acquiring sign language. These results show that individuality manifests itself from very early on in human development. Articulation in adults, both in speech and sign, is studied from two perspectives: normal production and re-learning production when the apparatus has been changed. Normal production is studied both in speech and sign and the effects of changed articulation are studied with regards to speech. Both these studies are done by using carrier sentences. Furthermore, sign production is studied giving the informants possibility for spontaneous speech. The production data from the signing informants is also used as the basis for input in the sign synthesis stimuli used in sign perception test battery. Speech and sign perception were studied using the informants’ answers to questions using forced choice in identification and discrimination tasks. These answers were then compared across language modalities. Three different informant groups participated in the sign perception tests: native signers, sign language interpreters and Finnish adults with no knowledge of any signed language. This gave a chance to investigate which of the characteristics found in the results were due to the language per se and which were due to the changes in modality itself. As the analogous test batteries yielded similar results over different informant groups, some common threads of results could be observed. Starting from very early on in acquiring speech and sign the results were highly individual. However, the results were the same within one individual when the same test was repeated. This individuality of results represented along same patterns across different language modalities and - in some occasions - across language groups. As both modalities yield similar answers to analogous study questions, this has lead us to providing methods for basic input for sign language applications, i.e. signing avatars. This has also given us answers to questions on precision of the animation and intelligibility for the users – what are the parameters that govern intelligibility of synthesised speech or sign and how precise must the animation or synthetic speech be in order for it to be intelligible. The results also give additional support to the well-known fact that intelligibility in fact is not the same as naturalness. In some cases, as shown within the sign perception test battery design, naturalness decreases intelligibility. This also has to be taken into consideration when designing applications. All in all, results from each of the test batteries, be they for signers or speakers, yield strikingly similar patterns, which would indicate yet further support for the common core for all human communication. Thus, we can modify and deepen the phonetic framework models for human communication based on the knowledge obtained from the results of the test batteries within this thesis.Siirretty Doriast

Size discrimination of transient signals

The importance of spectral cues in size discrimination of transient signals was investigated, and a model for this ability, tAIM, was created based on the biological principles of human hearing. A psychophysics experiment involving 40 participants found that the most important cue for size discrimination of transient signals, created by striking different sizes of polystyrene spheres, was similar to that of speakers listening to vowels – the relative positions of the resonances between comparison signals. It was found possible to scale the sphere signals in order to confuse listeners into believing the signal source was a different size, but two methods of scaling signals in order to sound the same size as another proved inconclusive, suggesting the possibility that transient signals cannot be scaled in a linear fashion as has been shown possible for vowels. Filtering the signals in a number of different ways found that the most important cue in size discrimination of transient signals is the difference between the most prominent resonances available in the spectra of the comparison signals. A model of the auditory system using the dynamic compressive Gammachirp filterbank, and based on the well-known AIM, was created to produce auditory images of transient signals that could be normalised for size. Transient-AIM, or tAIM used the Mellin transform to produce images that showed size normalisation was possible due to the spectral envelope similarities across the sizes of the spheres. tAIM was extended to carry out size discrimination of the spheres using the information contained within the Mellin images. There was a systematic association between Mellin phase and size of objects of various shapes, which suggests that tAIM is able to infer object size from sound recordings of objects being struck

MEG, PSYCHOPHYSICAL AND COMPUTATIONAL STUDIES OF LOUDNESS, TIMBRE, AND AUDIOVISUAL INTEGRATION

Natural scenes and ecological signals are inherently complex and understanding of their perception and processing is incomplete. For example, a speech signal contains not only information at various frequencies, but is also not static; the signal is concurrently modulated temporally. In addition, an auditory signal may be paired with additional sensory information, as in the case of audiovisual speech. In order to make sense of the signal, a human observer must process the information provided by low-level sensory systems and integrate it across sensory modalities and with cognitive information (e.g., object identification information, phonetic information). The observer must then create functional relationships between the signals encountered to form a coherent percept. The neuronal and cognitive mechanisms underlying this integration can be quantified in several ways: by taking physiological measurements, assessing behavioral output for a given task and modeling signal relationships. While ecological tokens are complex in a way that exceeds our current understanding, progress can be made by utilizing synthetic signals that encompass specific essential features of ecological signals. The experiments presented here cover five aspects of complex signal processing using approximations of ecological signals : (i) auditory integration of complex tones comprised of different frequencies and component power levels; (ii) audiovisual integration approximating that of human speech; (iii) behavioral measurement of signal discrimination; (iv) signal classification via simple computational analyses and (v) neuronal processing of synthesized auditory signals approximating speech tokens. To investigate neuronal processing, magnetoencephalography (MEG) is employed to assess cortical processing non-invasively. Behavioral measures are employed to evaluate observer acuity in signal discrimination and to test the limits of perceptual resolution. Computational methods are used to examine the relationships in perceptual space and physiological processing between synthetic auditory signals, using features of the signals themselves as well as biologically-motivated models of auditory representation. Together, the various methodologies and experimental paradigms advance the understanding of ecological signal analytics concerning the complex interactions in ecological signal structure

A Computational Model of Auditory Feature Extraction and Sound Classification

This thesis introduces a computer model that incorporates responses similar to those found in the cochlea, in sub-corticai auditory processing, and in auditory cortex. The principle aim of this work is to show that this can form the basis for a biologically plausible mechanism of auditory stimulus classification. We will show that this classification is robust to stimulus variation and time compression. In addition, the response of the system is shown to support multiple, concurrent, behaviourally relevant classifications of natural stimuli (speech). The model incorporates transient enhancement, an ensemble of spectro - temporal filters, and a simple measure analogous to the idea of visual salience to produce a quasi-static description of the stimulus suitable either for classification with an analogue artificial neural network or, using appropriate rate coding, a classifier based on artificial spiking neurons. We also show that the spectotemporal ensemble can be derived from a limited class of 'formative' stimuli, consistent with a developmental interpretation of ensemble formation. In addition, ensembles chosen on information theoretic grounds consist of filters with relatively simple geometries, which is consistent with reports of responses in mammalian thalamus and auditory cortex. A powerful feature of this approach is that the ensemble response, from which salient auditory events are identified, amounts to stimulus-ensemble driven method of segmentation which respects the envelope of the stimulus, and leads to a quasi-static representation of auditory events which is suitable for spike rate coding. We also present evidence that the encoded auditory events may form the basis of a representation-of-similarity, or second order isomorphism, which implies a representational space that respects similarity relationships between stimuli including novel stimuli

Open-set Speaker Identification

This study is motivated by the growing need for effective extraction of intelligence and evidence from audio recordings in the fight against crime, a need made ever more apparent with the recent expansion of criminal and terrorist organisations. The main focus is to enhance open-set speaker identification process within the speaker identification systems, which are affected by noisy audio data obtained under uncontrolled environments such as in the street, in restaurants or other places of businesses. Consequently, two investigations are initially carried out including the effects of environmental noise on the accuracy of open-set speaker recognition, which thoroughly cover relevant conditions in the considered application areas, such as variable training data length, background noise and real world noise, and the effects of short and varied duration reference data in open-set speaker recognition. The investigations led to a novel method termed “vowel boosting” to enhance the reliability in speaker identification when operating with varied duration speech data under uncontrolled conditions. Vowels naturally contain more speaker specific information. Therefore, by emphasising this natural phenomenon in speech data, it enables better identification performance. The traditional state-of-the-art GMM-UBMs and i-vectors are used to evaluate “vowel boosting”. The proposed approach boosts the impact of the vowels on the speaker scores, which improves the recognition accuracy for the specific case of open-set identification with short and varied duration of speech material