In this contribution we present a new approach for speech signal enhancement that improve a distorted speech signal in low SNR scenarios (at least if only a portion of the frequency range is highly disturbed). The approach consists of three algorithmic parts: a standard noise suppression unit, a partial speech reconstruction unit, and a time-frequency selective mixing unit. The basic idea of partial speech reconstruction is first to detect time-frequency areas where the SNR is still acceptable. These areas are used to extract relevant signal features such as the pitch frequency or the spectral envelope. Based on pre-trained signal models and the extracted features, the signal is reconstructed in a second stage. Finally, the reconstructed and the conventionally enhanced signals are mixed in a time-frequency selective manner. Subjective and objective tests indicate that a significant quality improvement is possible compared to conventional schemes - especially in high noise conditions.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Model-based speech enhancement for automotive applications


    Beteiligte:
    Krini, M. (Autor:in) / Schmidt, G. (Autor:in)


    Erscheinungsdatum :

    2009


    Format / Umfang :

    6 Seiten, 12 Quellen



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch





    Speech Response System for Automotive Applications

    Amir, Gideon | SAE Technical Papers | 1981


    Speech Response System for Automotive Applications

    Amir,G. / American Microsystems,Santa Clara,US | Kraftfahrwesen | 1981


    Automotive Speech Intelligibility Measurements

    Ebbitt, Gordon / Remtema, Todd | British Library Conference Proceedings | 2015


    Development of multi-lingual speech recognition and texted speech synthesis for automotive applications

    Deguchi, Y. / Kagoshima, T. / Hirabayashi, G. et al. | Tema Archiv | 2003