000			02716nam a22002657a 4500
003			KOHA
005			20241014092710.0
008			240924d2024 cy d a|| |||| 00| 0 eng d
040			_aCY-NiCIU _beng _cCY-NiCIU _erda
041			_aeng
090			_aYL 3365 _bA86 2024
100	1		_aAtosha, Pascal Bahavu
245	1	0	_aSPEECH RECOGNITION USING RECURRENT NEURAL NETWORK AND CONVOLUTIONAL NEURAL NETWORK / _cPASCAL BAHAVU ATOSHA ; SUPERVISOR, ASST. PROF. DR. EMRE ÖZBİLGE
264			_c2024
300			_a69 sheets : _c30 cm _e+1 CD ROM _billustrations, tables ;
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
502			_aThesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Computer Engineering
520			_aThe Recent years have seen tremendous advancements in speech recognition technology, which has become essential to many different applications, such as virtual assistants and transcription services. In order to improve the precision and resilience of speech recognition systems, this thesis investigates the combined use of recurrent neural networks (RNNs) and convolutional neural networks (CNNs). The study starts with a thorough analysis of the state-of-the-art speech recognition models, stressing the advantages and disadvantages of CNNs and RNNs. CNNs are skilled at obtaining organized characteristics based on spectrogram representations, whereas RNNs are best at gathering temporal dependencies in sequential data. This study suggests a combination of models that brings together the sequential learning skills of RNNs alongside the spatial feature mining prowess of CNNs, driven by their complementary strengths. Common metrics such as word error rate, match error rate, word information lost, and word information preserved were used to evaluate the performance of our combined model.With 0.2476 of word error rate, 0.0732 match error rate, 0.36 of word information lost, and 0.53 of word information preserved, our system achieved these results. The results of this research add to the current debate on the development of speech recognition technology by presenting a new method for combining the advantages of RNNs and CNNs in a way that maximizes their mutually beneficial impacts. For applications requiring accurate and reliable speech-to-text conversion, the proposed combined model shows promise, as speech recognition remains an essential part of interaction between humans and computers.
650		0	_aComputer Engineering _vDissertations, Academic
700	1		_aÖzbilge, Emre _esupervisor
942			_2ddc _cTS
999			_c292842 _d292842