@article{MAKHILLRJAS202116210288,
    title = {An Inclusive Survey for Text Dependent Automatic Speech Segmentation Techniques},
    journal = {Research Journal of Applied Sciences},
    volume = {16},
    number = {2},
    pages = {65-74},
    year = {2021},
    issn = {1815-932x},
    doi = {rjasci.2021.65.74},
    url = {https://makhillpublications.co/view-article.php?issn=1815-932x&doi=rjasci.2021.65.74},
    author = {Ihsan,Oumayma and},
    keywords = {ASR,TTS,phonetic segmentation,text-dependent,fusion,predictive models,speech parameterization,HMM},
    abstract = {Speech segmentation is the process of breaking
speech signal into distinct acoustic blocks that could be
words, syllabus or phonemes. Phonetic segmentation is
about finding the exact boundaries for the different
phonemes that composes a specific speech signal.
Phonetic segmentation is crucial for many applications
basically speech recognition ASR and speech to text
systems STT as ASR needs phonetically transcribed
training corpus, STT needs phoneme database. 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. In this study, we present a thorough survey of
the different algorithm and techniques proposed so far for
solving the problem of text-dependent phonetic
segmentation.}
    }