الفهرس 
Automatic Speaker RecognitionOnly 14 pages are availabe for public view
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Abstract
Reverberation effect is scientifically defined as the presence of sound in closed
rooms after removal of the sound source. It occurs due to multiple reflections from the
room walls, ceiling and ground. Both the shape and size of the room affect the reverberation
that occurs in the room. Reverberation generally spreads the energy of sound. This, in turn,
changes the speech signal characteristics such as pith frequency. It is known that the pitch
frequency is the fundamental frequency in the signal, and it is necessary to be determined
accurately for several applications. Different methods have been introduced in the literature
for pitch frequency estimation. These methods include Normalized Correlation Function
(NCF), Cepstrum Pitch Detection (CEP), Summation of Residual Harmonics (SRH) and
Pitch Estimation Filter (PEF). This thesis is concerned with the investigation of
reverberation effect on pitch frequency estimation. The studied methods for pitch
frequency estimation are compared in the presence of reverberation. It is found that the
PEF method is preferred for pitch frequency estimation in the presence of reverberation. In
addition, speaker identification is investigated in this thesis in the presence of
reverberation. Deep neural networks are investigated for this task as they are efficient tools
for feature extractions and classification. Speech signals are first transformed to
spectrograms, and then features are extracted from these spectrograms. Simulation results
proved good results for speaker identification in the presence of the reverberation effect.
Both text-dependent and text-independent recognition systems have been presented and
studied in the presence of degradation phenomena such as noise and reverberation. The
experimental results reveal that the recognition rates obtained for text-dependent speaker
recognition are higher than those of text-independent speaker recognition.