Neuro-inspired speech recognition based on reservoir computing

Arfan Ghani, T. M. McGinnity, Liam Maguire, Liam McDaid, Ammar Belatreche

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


    This chapter investigates the potential of recurrent spiking neurons for classification
    problems. It presents a hybrid approach based on the paradigm of Reservoir Computing. The
    practical applications based on recurrent spiking neurons are limited due to the lack of
    learning algorithms. Most of the previous work in the literature has focused on feed forward
    networks because computation in these networks is comparatively easy to analyse. The
    details of such networks have been reported in detail in (Haykin, 1999) (Pavlidis et al., 2005)
    (Bohte et al., 2000). Recently, a strategy proposed by Maass (Maass et al., 2002) and Jaeger
    (Jaeger, 2001) offers to overcome the burden of recurrent neural networks training. In this
    paradigm, instead of training the whole recurrent network only the output layer (known as
    readout neuron) is trained.
    This chapter investigates the potential of recurrent spiking neurons as the basic building
    blocks for the liquid or so called reservoir. These recurrent neural networks are termed as
    microcircuits which are viewed as basic computational units in cortical computation (Maass
    et al., 2002). These microcircuits are connected as columns which are linked with other
    neighboring columns in cortical areas. These columns read out information from each other
    and serve both as reservoir and readout. The reservoir is modeled as a dynamical system
    perturbed by the input stream where only readouts are trained to extract information from
    the reservoir. The basic motivation behind investigating recurrent neurons is their potential
    to memorise relevant events over short periods of time (Maass et al., 2002). The use of
    feedback enables recurrent networks to acquire state representation which makes them
    suitable for temporal based applications such as speech recognition. It is challenging to
    solve such problems with recurrent networks due to the burden of training. The paradigm
    of reservoir computing also referred to as liquid computing relaxes the burden of training
    because only an output layer is trained instead of training the whole network. The work
    presented in this chapter analyses the theoretical framework of Reservoir Computing and
    demonstrates results in terms of classification accuracy through the application of speech
    recognition. The design space for this paradigm is split into three domains; front end,
    reservoir, and back end. This work contributes to the identification of suitable front and
    back end processing techniques along with stable reservoir dynamics, which provides a
    reliable framework for classification related problems.
    The work presented in this chapter suggests a simple and efficient biologically plausible
    approach based on a hybrid implementation of recurrent spiking neurons and classical feed forward networks for an application of isolated digit recognition. The structure of this
    chapter is as follows: section 2 elaborates the motivation, related work, theoretical review
    and description of the paradigm of reservoir computing. Section 3 contains details about the
    experimental setup and investigates front-end pre-processing techniques and reservoir
    dynamics. A baseline feed forward classifier is described in section 4 and results are
    presented. Results based on reservoir recognition are presented in section 5. Section 6
    discusses results obtained through Poisson spike encoding. A thorough discussion and
    conclusion of the chapter is provided in section 7.
    Original languageEnglish
    Title of host publicationAdvances in Speech Recognition
    Number of pages21
    ISBN (Print)978-953-307-097-1
    Publication statusPublished - 2010


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