TY  - JOUR
T1  - A Survey: Text Independent Automatic Speech Segmentation Techniques
AU - Al-Hassani, Ihsan AU - Al-Dakkak, Oumayma AU - Assami, Abdlnaser 
JO  - Research Journal of Applied Sciences
VL  - 16
IS  - 2
SP  - 75
EP  - 84
PY  - 2021
DA  - 2001/08/19
SN  - 1815-932x
DO  - rjasci.2021.75.84
UR  - https://makhillpublications.co/view-article.php?doi=rjasci.2021.75.84
KW  - ASR
KW  -TTS
KW  -phonetic segmentation
KW  -text-independent
KW  -fusion
KW  -performance metrics
KW  -Query-by-Example (QbyE)
KW  -modeling
KW  -HMM
AB  - 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.
ER  -