Automatic speech recognition (ASR) systems are in evolutionary process. These systems use models which work at two stages, namely the parameterization of the input noise signal followed by training and testing of the features using any classification technique. Researchers have proposed a variety of acoustic models to accomplish this complex task. In this paper, we represent overview of Hidden Markov Model (HMM), Deep Neural Networks (DNNs) and Convolutional Neural Network (CNN) based models, which are the backbone of ASR systems. CNN is advanced method which normalizes speaker variance by using local filters in convolution layer. CNN architecture has advance features like weight sharing, local filters and pooling etc. This paper will also summarize the details of improvements in CNN s using different convolution techniques.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Acoustic modeling in Automatic Speech Recognition - A Survey


    Contributors:


    Publication date :

    2018-03-01


    Size :

    6705793 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    AUTOMATIC VEHICLE FOR SPEECH RECOGNITION COMPENSATION

    KIM JONG KYU / CHOI WI YEONG | European Patent Office | 2024

    Free access

    AUTOMATIC VEHICLE FOR SPEECH RECOGNITION COMPENSATION

    European Patent Office | 2024

    Free access

    Acoustic and Domain Based Speech Recognition For Vehicles

    JI AN / AMMAN SCOTT ANDREW / MORA RICHARDSON BRIGITTE FRANCES et al. | European Patent Office | 2017

    Free access

    Acoustic and domain based speech recognition for vehicles

    JI AN / AMMAN SCOTT ANDREW / MORA RICHARDSON BRIGITTE FRANCES et al. | European Patent Office | 2019

    Free access