Ihsan  Al-Hassani, Oumayma  Al-Dakkak, Abdlnaser  Assami, 
    A Survey: Text Independent Automatic Speech Segmentation Techniques,
    Research Journal of Applied Sciences,
    Volume 16,Issue 2,
    2021,
    Pages 75-84,
    ISSN 1815-932x,
    rjasci.2021.75.84,
    (https://makhillpublications.co/view-article.php?doi=rjasci.2021.75.84)
    Abstract: Speech segmentation techniques have made
important advances in the past decades and are still an
active area of research and development. It is a process of
breaking down a speech signal into smaller units such as
phonemes. Speech segmentation is decisive for many
acoustic systems essentially Automatic Speech
Recognition (ASR). Phonetic segmentation techniques are
divided into two major categories: Text-Dependent (TD)
and Text-Independent (TI). In the text-dependent
segmentation techniques, the phonetic annotation of the
speech signal is already known and we only need to find
the boundaries of each phoneme segment. While in the
Text-Independent (TI) techniques no annotation is
available, thus, the segmentation relies solely on the
acoustic information contained in the speech signal. In
this study, we present a thorough survey of the different
algorithms and techniques proposed so far for solving the
problem of text-independent phonetic segmentation.
    Keywords: ASR;TTS;phonetic segmentation;text-independent;fusion;performance metrics;Query-by-Example (QbyE);modeling;HMM