BOLD and perfusion changes during epileptic generalised spike wave activity

It is unclear whether neurovascular coupling is maintained during epileptic discharges. Knowing this is important to allow appropriate inferences from functional imaging studies of epileptic activity. Recent blood oxygen level-dependent (BOLD) functional MRI (fMRI) studies have demonstrated negative BOLD responses (NBR) in frontal, parietal and posterior cingulate cortices during generalised spike wave activity (GSW). We hypothesized that GSW-related NBR commonly reflect decreased cerebral blood flow (CBF). We measured BOLD and cerebral blood flow responses using simultaneous EEG with BOLD and arterial spin label (ASL) fMRI at 3 T. Four patients with epilepsy were studied; two with idiopathic generalized epilepsy (IGE) and two with secondary generalized epilepsy (SGE). We found GSW-related NBR in frontal, parietal and posterior cingulate cortices. We measured the coupling between BOLD and CBF changes during GSW and normal background EEG and found a positive correlation between the simultaneously measured BOLD and CBF throughout the imaged volume. Frontal and thalamic activation were seen in two patients with SGE, concordant with the electro-clinical features of their epilepsy. There was striking reproducibility of the GSW-associated BOLD response in subjects previously studied at 1.5 T. Our results show a preserved relationship between BOLD and CBF changes during rest and GSW activity consistent with normal neurovascular coupling in patients with generalized epilepsy and in particular during GSW activity. Cortical activations appear to reflect areas of discharge generation whilst deactivations reflect changes in conscious resting state activity

1 The Prosodic Marking of Phrase Boundaries: Expectations and Results

ABSTRACT Using sentence templates and a stochastic context-free grammar a large corpus (10,000 sentences) has been created, where prosodic phrase boundaries are labeled in the sentences automatically during sentence generation. With perception experiments on a subset of 500 utterances we verified that 92 % of the automatically marked boundaries were perceived as prosodically marked. In initial automatic classification experiments for three levels of boundaries recognition rates up to 81 % could be achieved. 1.1 Introduction and Material A successful automatic detection of phrase boundaries can be of great help for parsing a word hypotheses graph in an automatic speech understanding (ASU) system. Our recognition paradigm lies within the statistical approach; we therefore need a large training database, i.e. a corpus with reference labels for prosodically marked phrase boundaries. In this paper we wil

Automatic classification of prosodically marked phrase boundaries in German

A large corpus has been created automatically and read by speakers. Phrase boundaries were labeled in the sentences automatically during sentence generation. Perception experiments on a subset of 500 utterances showed a high agreement between the automatically generated boundary markers and the ones perceived by listeners. Gaussian distribution and polynomial classifiers were trained on a set of prosodic features computed from the speech signal using the automatically generated boundary markers. Comparing the classification results with the judgments of the listeners yielded in a recognition rate of 87%. A combination with stochastic language models improved the recognition rate to 90%. We found that the pause and the durational features are most important for the classification, but that the influence of F0 is not neglectable

2,4-Bis(diphenyl­phosphan­yl)-1,1,2,3,3,4-hexa­phenyl-1,3-diphospha-2,4-dibora­cyclo­butane tetra­hydro­furan sesqui­solvate

In the title compound, C60H50B2P4·1.5C4H8O, the diphospha­diborane mol­ecule lies on an inversion centre, whereas the disordered tetra­hydro­furan solvent mol­ecule is in a general position with a partial occupancy of 0.75. The diphosphadiborane mol­ecule consists of an ideal planar four-membered B2P2 ring with an additional phenyl and a –PPh2 group attached to each B atom

Pitch determination considering laryngealization effects in spoken dialogs

A frequent phenomenon in spoken dialogs of the information seeking type are short elliptic utterances whose mood (declarative or interrogative) can only be distinguished by intonation. The main acoustic evidence is conveyed by the fundamental frequency or Fo-contour. Many algorithms for Fo determination have been reported in the literature. A common problem are irregularities of speech known as "laryngealizations". This article describes an approach based on neural network techniques for the improved determination of fundamental frequency. First, an improved version of our neural network algorithm for reconstruction of the voice source signal (glottis signal) is presented. Second, the reconstructed voice source signal is used as input to another neural network distinguishing the three classes "voiceless", "voiced non-laryngealized", and "voiced laryngealized". Third, the results are used to improve an existing Fo algorithm. Results of this approach are presented and discussed in the context of the application in a spoken dialog system

Improving parsing of spontaneous speech with the help of prosodic boundaries

Parsing can be improved in automatic speech understanding if prosodic boundary marking is taken into account, because syntactic boundaries are often marked by prosodic means. Because large databases are needed for the training of statistical models for prosodic boundaries, we developed a labeling scheme for syntactic-prosodic boundaries within the German VERBMOBIL project (automatic speech-to-speech translation). We compare the results of classifiers (multi-layer perceptrons and language models) trained on these syntactic-prosodic boundary labels with classifiers trained on perceptual-prosodic and purely syntactic labels. Recognition rates of up to 96% were achieved. The turns that we need to parse consist of 20 words on the average and frequently contain sequences of partial sentence equivalents due to restarts, ellipsis, etc. For this material, the boundary scores computed by our classifiers can successfully be integrated into the syntactic parsing of word graphs; currently, they improve the parse time by 92% and reduce the number of parse trees by 96%. This is achieved by introducing a special Prosodic Syntactic Clause Boundary symbol (PSCB) into our grammar and guiding the search for the best word chain with the prosodic boundary scores

Going back to the source : inverse filtering of the speech signal with ANNs

In this paper we present a new method transforming speech signals to voice source signals (VSS) using artificial neural networks (ANN). We will point out that the ANN mapping of speech signals into source signals is quite accurate, and most of the irregularities in the speech signal will lead to an irregularity in the source signal, produced by the ANN (ANN-VSS). We will show that the mapping of the ANN is robust with respect to untrained speakers, different recording conditions and facilities, and different vocabularies. We will also present preliminary results which show that from the ANN source signal pitch periods can be determined accurately

"Roger", "Sorry", "I'm still listening" : dialog guiding signals in information retrieval dialogs

During any kind of information retrieval dialog, the repetition of parts of information just given by the dialog partner can often be observed. As these repetitions are usually elliptic, the intonation is very important for determining the speakers intention. In this paper prototypically the times of day repeated by the customer in train table inquiry dialogs are investigated. A scheme is developed for the officers reactions depending on the intonation of these repetitions; it has been integrated into our speech understanding and dialog system EVAR (cf. [6]). Gaussian classifiers were trained for distinguishing the dialog guiding signals confirmation, question and feedback; recognition rates of up to 87.5% were obtained

Characterisation of voice quality of Parkinson’s disease using differential phonological posterior features

Change in voice quality (VQ) is one of the first precursors of Parkinson’s disease (PD). Specifically, impacted phonation and articulation causes the patient to have a breathy, husky-semiwhisper and hoarse voice. A goal of this paper is to characterise a VQ spectrum – the composition of non-modal phonations – of voice in PD. The paper relates non-modal healthy phonations: breathy, creaky, tense, falsetto and harsh, with disordered phonation in PD. First, statistics are learned to differentiate the modal and non-modal phonations. Statistics are computed using phonological posteriors, the probabilities of phonological features inferred from the speech signal using a deep learning approach. Second, statistics of disordered speech are learned from PD speech data comprising 50 patients and 50 healthy controls. Third, Euclidean distance is used to calculate similarity of non-modal and disordered statistics, and the inverse of the distances is used to obtain the composition of non-modal phonation in PD. Thus, pathological voice quality is characterised using healthy non-modal voice quality “base/eigenspace”. The obtained results are interpreted as the voice of an average patient with PD and can be characterised by the voice quality spectrum composed of 30% breathy voice, 23% creaky voice, 20% tense voice, 15% falsetto voice and 12% harsh voice. In addition, the proposed features were applied for prediction of the dysarthria level according to the Frenchay assessment score related to the larynx, and significant improvement is obtained for reading speech task. The proposed characterisation of VQ might also be applied to other kinds of pathological speech