Desynchronization of pathological low-frequency brain activity by the hypnotic drug zolpidem.

Reports of the beneficial effects of the hypnotic imidazopyridine, zolpidem, described in persistent vegetative state^1, 2^ have been replicated recently in brain-injured and cognitively impaired patients^3-7^. Previous single photon emission computed tomography (SPECT) studies have suggested that sub-sedative doses of zolpidem increased regional cerebral perfusion in affected areas^5, 8^, implying enhanced neuronal metabolic activity; which has led to speculation that zolpidem 'reawakens' functionally dormant cortex. However, a neuronal mechanism by which this hypnotic drug affords benefits to brain injured patients has yet to be demonstrated. Here, we report the action of sub-sedative doses of zolpidem on neuronal network oscillatory activity in human brain, measured using pharmaco-magnetoencephalography (pharmaco-MEG). Study participant JP suffered a stroke in 1996, causing major damage to the left hemisphere that impaired aspects of both motor and cognitive function. Pharmaco-MEG analyses revealed robust and persistent pathological theta (4-10Hz) and beta (15-30Hz) oscillations within the lesion penumbra and surrounding cortex. Administration of zolpidem (5mg) reduced the power of pathological theta and beta oscillations in all regions of the lesioned hemisphere. This desynchronizing effect correlated well with zolpidem uptake (occurring approximately 40 minutes after acute administration) and was coincident with marked improvements in cognitive and motor function. Control experiments revealed no effect of placebo, while a structurally unrelated hypnotic, zopiclone, administered at a comparable dose (3.5mg) elicited widespread increases in cortical oscillatory power in the beta (15-30Hz) band without functional improvement. These results suggest that in JP, specific motor and cognitive impairments are related to increased low-frequency oscillatory neuronal network activity. Zolpidem is unique amongst hypnotic drugs in its ability to desynchronize such pathological low-frequency activity, thereby restoring cognitive function

The right information may matter more than frequency-place alignment: Simulations of frequency-aligned and upward shifting cochlear implant processors for a shallow electrode array insertion

Objective: It has been claimed that speech recognition with a cochlear implant is dependent on the correct frequency alignment of analysis bands in the speech processor with characteristic frequencies (CFs) at electrode locations. However, the use of filters aligned in frequency to a relatively basal electrode array position leads to significant loss of lower frequency speech information. This study uses an acoustic simulation to compare two approaches to the matching of speech processor filters to an electrode array having a relatively shallow depth within the typical range, such that the most apical element is at a CF of 1851 Hz. Two noise-excited vocoder speech processors are compared, one with CF-matched filters, and one with filters matched to CFs at basilar membrane locations 6 mm more apical than electrode locations.Design: An extended crossover training design examined pre- and post-training performance in the identification of vowels and words in sentences for both processors. Subjects received about 3 hours of training with each processor in turn.Results: Training improved performance with both processors, but training effects were greater for the shifted processor. For a male talker, the shifted processor led to higher post-training scores than the frequency-aligned processor with both vowels and sentences. For a female talker, post-training vowel scores did not differ significantly between processors, whereas sentence scores were higher with the frequency-aligned processor.Conclusions: Even for a shallow electrode insertion, we conclude that a speech processor should represent information from important frequency regions below 1 kHz and that the possible cost of frequency misalignment can be significantly reduced with listening experience

Effects of noise suppression and envelope dynamic range compression on the intelligibility of vocoded sentences for a tonal language

Vocoder simulation studies have suggested that the carrier signal type employed affects the intelligibility of vocoded speech. The present work further assessed how carrier signal type interacts with additional signal processing, namely, single-channel noise suppression and envelope dynamic range compression, in determining the intelligibility of vocoder simulations. In Experiment 1, Mandarin sentences that had been corrupted by speech spectrum-shaped noise (SSN) or two-talker babble (2TB) were processed by one of four single-channel noise-suppression algorithms before undergoing tone-vocoded (TV) or noise-vocoded (NV) processing. In Experiment 2, dynamic ranges of multiband envelope waveforms were compressed by scaling of the mean-removed envelope waveforms with a compression factor before undergoing TV or NV processing. TV Mandarin sentences yielded higher intelligibility scores with normal-hearing (NH) listeners than did noise-vocoded sentences. The intelligibility advantage of noise-suppressed vocoded speech depended on the masker type (SSN vs 2TB). NV speech was more negatively influenced by envelope dynamic range compression than was TV speech. These findings suggest that an interactional effect exists between the carrier signal type employed in the vocoding process and envelope distortion caused by signal processing

The listening talker: A review of human and algorithmic context-induced modifications of speech

International audienceSpeech output technology is finding widespread application, including in scenarios where intelligibility might be compromised - at least for some listeners - by adverse conditions. Unlike most current algorithms, talkers continually adapt their speech patterns as a response to the immediate context of spoken communication, where the type of interlocutor and the environment are the dominant situational factors influencing speech production. Observations of talker behaviour can motivate the design of more robust speech output algorithms. Starting with a listener-oriented categorisation of possible goals for speech modification, this review article summarises the extensive set of behavioural findings related to human speech modification, identifies which factors appear to be beneficial, and goes on to examine previous computational attempts to improve intelligibility in noise. The review concludes by tabulating 46 speech modifications, many of which have yet to be perceptually or algorithmically evaluated. Consequently, the review provides a roadmap for future work in improving the robustness of speech output

Speaker Normalization Using Cortical Strip Maps: A Neural Model for Steady State vowel Categorization

Auditory signals of speech are speaker-dependent, but representations of language meaning are speaker-independent. The transformation from speaker-dependent to speaker-independent language representations enables speech to be learned and understood from different speakers. A neural model is presented that performs speaker normalization to generate a pitch-independent representation of speech sounds, while also preserving information about speaker identity. This speaker-invariant representation is categorized into unitized speech items, which input to sequential working memories whose distributed patterns can be categorized, or chunked, into syllable and word representations. The proposed model fits into an emerging model of auditory streaming and speech categorization. The auditory streaming and speaker normalization parts of the model both use multiple strip representations and asymmetric competitive circuits, thereby suggesting that these two circuits arose from similar neural designs. The normalized speech items are rapidly categorized and stably remembered by Adaptive Resonance Theory circuits. Simulations use synthesized steady-state vowels from the Peterson and Barney [J. Acoust. Soc. Am. 24, 175-184 (1952)] vowel database and achieve accuracy rates similar to those achieved by human listeners. These results are compared to behavioral data and other speaker normalization models.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

A Framework for Bioacoustic Vocalization Analysis Using Hidden Markov Models

Using Hidden Markov Models (HMMs) as a recognition framework for automatic classification of animal vocalizations has a number of benefits, including the ability to handle duration variability through nonlinear time alignment, the ability to incorporate complex language or recognition constraints, and easy extendibility to continuous recognition and detection domains. In this work, we apply HMMs to several different species and bioacoustic tasks using generalized spectral features that can be easily adjusted across species and HMM network topologies suited to each task. This experimental work includes a simple call type classification task using one HMM per vocalization for repertoire analysis of Asian elephants, a language-constrained song recognition task using syllable models as base units for ortolan bunting vocalizations, and a stress stimulus differentiation task in poultry vocalizations using a non-sequential model via a one-state HMM with Gaussian mixtures. Results show strong performance across all tasks and illustrate the flexibility of the HMM framework for a variety of species, vocalization types, and analysis tasks