Linking Speech Perception and Neurophysiology: Speech Decoding Guided by Cascaded Oscillators Locked to the Input Rhythm

The premise of this study is that current models of speech perception, which are driven by acoustic features alone, are incomplete, and that the role of decoding time during memory access must be incorporated to account for the patterns of observed recognition phenomena. It is postulated that decoding time is governed by a cascade of neuronal oscillators, which guide template-matching operations at a hierarchy of temporal scales. Cascaded cortical oscillations in the theta, beta, and gamma frequency bands are argued to be crucial for speech intelligibility. Intelligibility is high so long as these oscillations remain phase locked to the auditory input rhythm. A model (Tempo) is presented which is capable of emulating recent psychophysical data on the intelligibility of speech sentences as a function of “packaging” rate (Ghitza and Greenberg, 2009). The data show that intelligibility of speech that is time-compressed by a factor of 3 (i.e., a high syllabic rate) is poor (above 50% word error rate), but is substantially restored when the information stream is re-packaged by the insertion of silent gaps in between successive compressed-signal intervals – a counterintuitive finding, difficult to explain using classical models of speech perception, but emerging naturally from the Tempo architecture

Representation of Time-Varying Stimuli by a Network Exhibiting Oscillations on a Faster Time Scale

Sensory processing is associated with gamma frequency oscillations (30–80 Hz) in sensory cortices. This raises the question whether gamma oscillations can be directly involved in the representation of time-varying stimuli, including stimuli whose time scale is longer than a gamma cycle. We are interested in the ability of the system to reliably distinguish different stimuli while being robust to stimulus variations such as uniform time-warp. We address this issue with a dynamical model of spiking neurons and study the response to an asymmetric sawtooth input current over a range of shape parameters. These parameters describe how fast the input current rises and falls in time. Our network consists of inhibitory and excitatory populations that are sufficient for generating oscillations in the gamma range. The oscillations period is about one-third of the stimulus duration. Embedded in this network is a subpopulation of excitatory cells that respond to the sawtooth stimulus and a subpopulation of cells that respond to an onset cue. The intrinsic gamma oscillations generate a temporally sparse code for the external stimuli. In this code, an excitatory cell may fire a single spike during a gamma cycle, depending on its tuning properties and on the temporal structure of the specific input; the identity of the stimulus is coded by the list of excitatory cells that fire during each cycle. We quantify the properties of this representation in a series of simulations and show that the sparseness of the code makes it robust to uniform warping of the time scale. We find that resetting of the oscillation phase at stimulus onset is important for a reliable representation of the stimulus and that there is a tradeoff between the resolution of the neural representation of the stimulus and robustness to time-warp. Author Summary Sensory processing of time-varying stimuli, such as speech, is associated with high-frequency oscillatory cortical activity, the functional significance of which is still unknown. One possibility is that the oscillations are part of a stimulus-encoding mechanism. Here, we investigate a computational model of such a mechanism, a spiking neuronal network whose intrinsic oscillations interact with external input (waveforms simulating short speech segments in a single acoustic frequency band) to encode stimuli that extend over a time interval longer than the oscillation's period. The network implements a temporally sparse encoding, whose robustness to time warping and neuronal noise we quantify. To our knowledge, this study is the first to demonstrate that a biophysically plausible model of oscillations occurring in the processing of auditory input may generate a representation of signals that span multiple oscillation cycles.National Science Foundation (DMS-0211505); Burroughs Wellcome Fund; U.S. Air Force Office of Scientific Researc

Acoustic-driven delta rhythms as prosodic markers

<p>Oscillation-based models of speech perception postulate a cortical computation principle by which decoding is performed within a time-varying window structure, synchronised with the input on multiple time scales. The windows are generated by a segmentation process, implemented by a cascade of oscillators. This paper tests the hypothesis that prosodic segmentation is driven by a “flexible” (in contrast to autonomous, “rigid”) oscillator in the delta range (0.5–3 Hz) by tracking prosodic rhythms, such that intelligibility is impaired when the ability of this oscillator to synchronise to these rhythms is impaired. In setting phrasal boundaries, both bottom-up acoustic-driven and top-down context-invoked processes interact in a manner that is difficult to decompose. The present experiments used context-free random-digit strings in order to focus exclusively on bottom-up processes. Two experiments are reported. Listeners performed a target identification task, listening to stimuli with prescribed chunking patterns (Experiment I) or chunking rates (Experiment II), followed by a target. Irrespective of the chunking pattern, performance is high only for targets inside of a chunk, pointing to the benefit of acoustic prosodic segmentation in digit retrieval. Importantly, performance remains high as long as the chunking rate is within the frequency range of neuronal delta, but sharply deteriorates for higher rates. This data provides psychophysical evidence for the role of acoustic-driven segmentation, with flexible delta oscillations at the core, in digit retrieval.</p