22 resultados para SPEECH BULB

em National Center for Biotechnology Information - NCBI


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We have applied functional MRI (fMRI) based on blood oxygenation level-dependent (BOLD) image-contrast to map odor-elicited olfactory responses at the laminar level in the rat olfactory bulb (OB) elicited by iso-amyl acetate (10−2 dilution of saturated vapor) with spatial and temporal resolutions of 220×220×1,000 μm and 36 s. The laminar structure of the OB was clearly depicted by high-resolution in vivo anatomical MRI with spatial resolution of 110×110×1,000 μm. In repeated BOLD fMRI measurements, highly significant (P < 0.001) foci were located in the outer layers of both OBs. The occurrence of focal OB activity within a domain at the level of individual glomeruli or groups of glomeruli was corroborated on an intra- and inter-animal basis under anesthetized conditions with this noninvasive method. The dynamic studies demonstrated that the odor-elicited BOLD activations were highly reproducible on a time scale of minutes, whereas over tens of minutes the activations sometimes varied slowly. We found large BOLD signal (ΔS/S = 10–30%) arising from the olfactory nerve layer, which is devoid of synapses and composed of unmyelinated fibers and glial cells. Our results support previous studies with other methods showing that odors elicit activity within glomerular layer domains in the mammalian OB, and extend the analysis to shorter time periods at the level of individual glomeruli or groups of glomeruli. With further improvement, BOLD fMRI should be ideal for systematic analysis of the functional significance of individual glomeruli in olfactory information encoding and of spatiotemporal processing within the olfactory system.

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Investigation of the three-generation KE family, half of whose members are affected by a pronounced verbal dyspraxia, has led to identification of their core deficit as one involving sequential articulation and orofacial praxis. A positron emission tomography activation study revealed functional abnormalities in both cortical and subcortical motor-related areas of the frontal lobe, while quantitative analyses of magnetic resonance imaging scans revealed structural abnormalities in several of these same areas, particularly the caudate nucleus, which was found to be abnormally small bilaterally. A recent linkage study [Fisher, S., Vargha-Khadem, F., Watkins, K. E., Monaco, A. P. & Pembry, M. E. (1998) Nat. Genet. 18, 168–170] localized the abnormal gene (SPCH1) to a 5.6-centiMorgan interval in the chromosomal band 7q31. The genetic mutation or deletion in this region has resulted in the abnormal development of several brain areas that appear to be critical for both orofacial movements and sequential articulation, leading to marked disruption of speech and expressive language.

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In adult rodents, neurons are continually generated in the subventricular zone of the forebrain, from where they migrate tangentially toward the olfactory bulb, the only known target for these neuronal precursors. Within the main olfactory bulb, they ascend radially into the granule and periglomerular cell layers, where they differentiate mainly into local interneurons. The functional consequences of this permanent generation and integration of new neurons into existing circuits are unknown. To address this question, we used neural cell adhesion molecule-deficient mice that have documented deficits in the migration of olfactory-bulb neuron precursors, leading to about 40% size reduction of this structure. Our anatomical study reveals that this reduction is restricted to the granule cell layer, a structure that contains exclusively γ-aminobutyric acid (GABA)ergic interneurons. Furthermore, mutant mice were subjected to experiments designed to examine the behavioral consequences of such anatomical alteration. We found that the specific reduction in the newly generated interneuron population resulted in an impairment of discrimination between odors. In contrast, both the detection thresholds for odors and short-term olfactory memory were unaltered, demonstrating that a critical number of bulbar granule cells is crucial only for odor discrimination but not for general olfactory functions.

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The γ-aminobutyric acid type A (GABAA) receptor is the predominant Cl− channel protein mediating inhibition in the olfactory bulb and elsewhere in the mammalian brain. The olfactory bulb is rich in neurons containing both GABA and dopamine. Dopamine D1 and D2 receptors are also highly expressed in this brain region with a distinct and complementary distribution pattern. This distribution suggests that dopamine may control the GABAergic inhibitory processing of odor signals, possibly via different signal-transduction mechanisms. We have observed that GABAA receptors in the rat olfactory bulb are differentially modulated by dopamine in a cell-specific manner. Dopamine reduced the currents through GABA-gated Cl- channels in the interneurons, presumably granule cells. This action was mediated via D1 receptors and involved phosphorylation of GABAA receptors by protein kinase A. Enhancement of GABA responses via activation of D2 dopamine receptors and phosphorylation of GABAA receptors by protein kinase C was observed in mitral/tufted cells. Decreasing or increasing the binding affinity for GABA appears to underlie the modulatory effects of dopamine via distinct receptor subtypes. This dual action of dopamine on inhibitory GABAA receptor function in the rat olfactory bulb could be instrumental in odor detection and discrimination, olfactory learning, and ultimately odotopic memory formation.

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Dynamic blood oxygenation level-dependent functional MRI was applied at 7 T in the rat olfactory bulb (OB) with pulsed delivery of iso-amyl acetate (IAA) and limonene. Acquisition times for single-slice and whole OB data were 8 and 32 s, respectively, with spatial resolution of 220 × 220 × 250 μm. On an intrasubject basis, short IAA exposures of 0.6 min separated by 3.5-min intervals induced reproducible spatial activity patterns (SAPs) in the olfactory nerve layer, glomerular layer, and external plexiform layer. During long exposures (≈10 min), the initially dominant dorsal SAPs declined in intensity and area, whereas in some OB regions, the initially weak ventral/lateral SAPs increased first and then decreased. The SAPs of different concentrations were topologically similar, which implies that whereas an odor at various concentrations activates the same subsets of receptor cells, different concentrations are assessed and discriminated by variable magnitudes of laminarspecific activations. IAA and limonene reproducibly activated different subsets of receptor cells with some overlaps. Whereas qualitative topographical agreement was observed with results from other methods, the current dynamic blood oxygenation level-dependent functional MRI results can provide quantitative SAPs of the entire OB.

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Spoken language is one of the most compact and structured ways to convey information. The linguistic ability to structure individual words into larger sentence units permits speakers to express a nearly unlimited range of meanings. This ability is rooted in speakers' knowledge of syntax and in the corresponding process of syntactic encoding. Syntactic encoding is highly automatized, operates largely outside of conscious awareness, and overlaps closely in time with several other processes of language production. With the use of positron emission tomography we investigated the cortical activations during spoken language production that are related to the syntactic encoding process. In the paradigm of restrictive scene description, utterances varying in complexity of syntactic encoding were elicited. Results provided evidence that the left Rolandic operculum, caudally adjacent to Broca's area, is involved in both sentence-level and local (phrase-level) syntactic encoding during speaking.

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Neuronal synchronization in the olfactory bulb has been proposed to arise from a diffuse action of glutamate released from mitral cells (MC, olfactory bulb relay neurons). According to this hypothesis, glutamate spills over from dendrodendritic synapses formed between MC and granule cells (GC, olfactory bulb interneurons) to activate neighboring MC. The excitation of MC is balanced by a strong inhibition from GC. Here we show that MC excitation is caused by glutamate released from bulbar interneurons located in the GC layer. These reciprocal synapses depend on an unusual, 2-amino-5-phosphonovaleric acid-resistant, N-methyl-d-aspartate receptor. This type of feedback excitation onto relay neurons may strengthen the original sensory input signal and further extend the function of the dendritic microcircuit within the main olfactory bulb.

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Lesions to left frontal cortex in humans produce speech production impairments (nonfluent aphasia). These impairments vary from subject to subject and performance on certain speech production tasks can be relatively preserved in some patients. A possible explanation for preservation of function under these circumstances is that areas outside left prefrontal cortex are used to compensate for the injured brain area. We report here a direct demonstration of preserved language function in a stroke patient (LF1) apparently due to the activation of a compensatory brain pathway. We used functional brain imaging with positron emission tomography (PET) as a basis for this study.

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Computer speech synthesis has reached a high level of performance, with increasingly sophisticated models of linguistic structure, low error rates in text analysis, and high intelligibility in synthesis from phonemic input. Mass market applications are beginning to appear. However, the results are still not good enough for the ubiquitous application that such technology will eventually have. A number of alternative directions of current research aim at the ultimate goal of fully natural synthetic speech. One especially promising trend is the systematic optimization of large synthesis systems with respect to formal criteria of evaluation. Speech recognition has progressed rapidly in the past decade through such approaches, and it seems likely that their application in synthesis will produce similar improvements.

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The term "speech synthesis" has been used for diverse technical approaches. In this paper, some of the approaches used to generate synthetic speech in a text-to-speech system are reviewed, and some of the basic motivations for choosing one method over another are discussed. It is important to keep in mind, however, that speech synthesis models are needed not just for speech generation but to help us understand how speech is created, or even how articulation can explain language structure. General issues such as the synthesis of different voices, accents, and multiple languages are discussed as special challenges facing the speech synthesis community.

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Advances in digital speech processing are now supporting application and deployment of a variety of speech technologies for human/machine communication. In fact, new businesses are rapidly forming about these technologies. But these capabilities are of little use unless society can afford them. Happily, explosive advances in microelectronics over the past two decades have assured affordable access to this sophistication as well as to the underlying computing technology. The research challenges in speech processing remain in the traditionally identified areas of recognition, synthesis, and coding. These three areas have typically been addressed individually, often with significant isolation among the efforts. But they are all facets of the same fundamental issue--how to represent and quantify the information in the speech signal. This implies deeper understanding of the physics of speech production, the constraints that the conventions of language impose, and the mechanism for information processing in the auditory system. In ongoing research, therefore, we seek more accurate models of speech generation, better computational formulations of language, and realistic perceptual guides for speech processing--along with ways to coalesce the fundamental issues of recognition, synthesis, and coding. Successful solution will yield the long-sought dictation machine, high-quality synthesis from text, and the ultimate in low bit-rate transmission of speech. It will also open the door to language-translating telephony, where the synthetic foreign translation can be in the voice of the originating talker.

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The conversion of text to speech is seen as an analysis of the input text to obtain a common underlying linguistic description, followed by a synthesis of the output speech waveform from this fundamental specification. Hence, the comprehensive linguistic structure serving as the substrate for an utterance must be discovered by analysis from the text. The pronunciation of individual words in unrestricted text is determined by morphological analysis or letter-to-sound conversion, followed by specification of the word-level stress contour. In addition, many text character strings, such as titles, numbers, and acronyms, are abbreviations for normal words, which must be derived. To further refine these pronunciations and to discover the prosodic structure of the utterance, word part of speech must be computed, followed by a phrase-level parsing. From this structure the prosodic structure of the utterance can be determined, which is needed in order to specify the durational framework and fundamental frequency contour of the utterance. In discourse contexts, several factors such as the specification of new and old information, contrast, and pronominal reference can be used to further modify the prosodic specification. When the prosodic correlates have been computed and the segmental sequence is assembled, a complete input suitable for speech synthesis has been determined. Lastly, multilingual systems utilizing rule frameworks are mentioned, and future directions are characterized.

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This paper introduces the session on advanced speech recognition technology. The two papers comprising this session argue that current technology yields a performance that is only an order of magnitude in error rate away from human performance and that incremental improvements will bring us to that desired level. I argue that, to the contrary, present performance is far removed from human performance and a revolution in our thinking is required to achieve the goal. It is further asserted that to bring about the revolution more effort should be expended on basic research and less on trying to prematurely commercialize a deficient technology.

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In the past decade, tremendous advances in the state of the art of automatic speech recognition by machine have taken place. A reduction in the word error rate by more than a factor of 5 and an increase in recognition speeds by several orders of magnitude (brought about by a combination of faster recognition search algorithms and more powerful computers), have combined to make high-accuracy, speaker-independent, continuous speech recognition for large vocabularies possible in real time, on off-the-shelf workstations, without the aid of special hardware. These advances promise to make speech recognition technology readily available to the general public. This paper focuses on the speech recognition advances made through better speech modeling techniques, chiefly through more accurate mathematical modeling of speech sounds.