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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Acoustic modeling in Automatic Speech Recognition - A Survey


    Beteiligte:
    Waris, Aqbal (Autor:in) / Aggarwal, R.K (Autor:in)


    Erscheinungsdatum :

    2018-03-01


    Format / Umfang :

    6705793 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    AUTOMATIC VEHICLE FOR SPEECH RECOGNITION COMPENSATION

    KIM JONG KYU / CHOI WI YEONG | Europäisches Patentamt | 2024

    Freier Zugriff

    Acoustic and domain based speech recognition for vehicles

    Europäisches Patentamt | 2017

    Freier Zugriff

    Acoustic and domain based speech recognition for vehicles

    JI AN / AMMAN SCOTT ANDREW / MORA RICHARDSON BRIGITTE FRANCES et al. | Europäisches Patentamt | 2019

    Freier Zugriff

    Acoustic and Domain Based Speech Recognition For Vehicles

    JI AN / AMMAN SCOTT ANDREW / MORA RICHARDSON BRIGITTE FRANCES et al. | Europäisches Patentamt | 2017

    Freier Zugriff

    Automatic positioning speech recognition system and method

    LUO HENG / FENG KUN | Europäisches Patentamt | 2021

    Freier Zugriff