Abstract
We analyse the properties of the physiological model of the adaptive behaviour of the chemical synapse between inner hair cells (IHC) and auditory neurons. On the basis of the performed analysis, we propose equivalent structures of the model for implementation in the digital domain. The main conclusion of the analysis is that the synapse reservoir model is equivalent in its properties to the signal-dependent automatic gain-control mechanism. We plot guidelines for creation of artificial anthropomorphic algorithms, which exploit properties of the original synapse model. This paper also presents a concise description of the experiments, which prove the presence of the positive effect from the introduction of the depicted anthropomorphic algorithm into feature extraction of the automated speech recognition engine.
Highlights
Physiological research into the way the inner ear converts an acoustical stimulation into a response of the auditory nerve fibers among many other findings led to the conclusions that (i) inner hair cells are mechanical vibrations sensory cells; (ii) each IHC makes chemical synapses with approximately 10–30 peripheral axons of primary bipolar neurons which cell bodies contained in the spiral ganglion and modiolar axons forming the auditory (VIIIth) nerve; (iii) one can distinguish three groups of afferent neurons based on the level of their spontaneous activity: low-spontaneous rate, medium-spontaneous rate, and high-spontaneous rate fibers
Analysis of the physiological model of the chemical IHC-auditory nerve (AN) synapse creates an opportunity to implement it in the form of the anthropomorphic algorithm, which is computationally efficient and may be used in technical devices
It was found that effect of the IHC adaptation model is equivalent to the action of signal-dependent automatic gain control mechanism
Summary
Many contemporary speech processing techniques tend to reflect properties of the human auditory apparatus. While the first two mentioned branches are concentrated on the closest possible literal reproduction of the auditory apparatus properties in the artificial device, the latter imply a computationally efficient way to implement the “biological” audio processing algorithm with a certain predefined precision. Before the employment of a certain physiological model into the mentioned applications, one should answer the questions of why it is important (i.e., what result is expected from it) and what is the most efficient way of its implementation. This reasoning leads to a conclusion that the further analysis of the available physiological models with the aim of finding their algorithmical interpretation is needed. This “adaptive strategy” at first glance seems to be advantageous since it allows an emphasis of nonstationarities within the incoming signal
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