@article{MAKHILLRJAS202116210289,
    title = {A Survey: Text Independent Automatic Speech Segmentation Techniques},
    journal = {Research Journal of Applied Sciences},
    volume = {16},
    number = {2},
    pages = {75-84},
    year = {2021},
    issn = {1815-932x},
    doi = {rjasci.2021.75.84},
    url = {https://makhillpublications.co/view-article.php?issn=1815-932x&doi=rjasci.2021.75.84},
    author = {Ihsan,Oumayma and},
    keywords = {ASR,TTS,phonetic segmentation,text-independent,fusion,performance metrics,Query-by-Example (QbyE),modeling,HMM},
    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.}
    }