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

By Oded Ghitza. Front. Psychology, 2011

359 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/80f31d784df29d2e62b1dcd9d0da8e4f635447c9

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Citations in the corpus

Speech Rhythms and Multiplexed Oscillatory Sensory Coding in the Human Brain (2013, background): “Our study supports emerging models of speech perception that emphasise the role of brain oscillations [9,12].”
Distinct contributions of low- and high-frequency neural oscillations to speech comprehension (2017, background): “the read-out of speech processing (Ghitza, 2011; Giraud & Poeppel, 2012; Mesgarani et al., 2014; Poeppel, 2003; Poeppel et al., 2008).”
Unsupervised word discovery from speech using automatic segmentation into syllable-like units (2015, methodology): “Conceptual models of speech perception by Ghitza [20] and Giraud and Poeppel [21] explicitly posit that these so-called”
Mechanisms Underlying Selective Neuronal Tracking of Attended Speech at a ‘Cocktail Party’ (2013, background): “…(ECoG) reflect the synaptic activity that underpins neuronal firing (Buzsaki, 2006), there is likely to be amechanistic relationship between these two speech tracking indices (Ghitza, 2011; Giraud and Poeppel, 2012; Nourski et al., 2009); however, the details are not well understood.”
Acoustic landmarks drive delta-theta oscillations to enable speech comprehension by facilitating perceptual parsing (2013, methodology): “…type of multi-time resolution analysis and integration could be performed in auditory cortex using neuronal oscillations – corresponding to these two temporal windows of integration (~5 Hz, theta; ~40 Hz, gamma) – to parse the sound input at these separate timescales (Ghitza, 2011; Poeppel, 2003).”
The Theta-Gamma Neural Code (2013, positive): “In the auditory system, a model has been proposed in which ongoing syllables and words are segmented by an oscillatory cortical decoder based on nested theta and gamma oscillations (Ghitza, 2011; Giraud and Poeppel, 2012; Peelle and Davis, 2012).”
The human auditory cortex concurrently tracks syllabic and phonemic timescales via acoustic spectral flux (2024, unknown): “This mechanism is proposed to occur in parallel with the parsing of syllables through a phase-amplitude coupling (PAC) mechanism, enabling efficient analysis of speech via a dual timescale processing mode ( 4 , 5 , 38 ).”
Back in time: Reversed speech as a window into cognitive language processing—A systematic review (2026, unknown): “Coupled oscillator models of speech processing (Ghitza, 2011; Giraud & Poeppel, 2012) with cross-frequency coupling (Giraud & Poeppel, 2012; Hyafil et al., 2015) may accommodate both aspects of reversed speech.”