TUFEKCI, z., and GOWDY, J.N.: ‘Feature extraction using discrete wavelet transform for speech recognition’. Proc. IEEE Southeastcon 2000, Nashville, USA, 2000, pp. 116-123 FAROOQ, o., and DATTA, s.: ‘Dynamic feature extraction by wavelet analysis’. Proc. 6th Int. Conf. Spoken Language Processing, Beijing, China, Oct. 2000, Vol. 4, pp. 696-699 CHANG, s., KWON, Y . , and YANG, S I . : ‘Speech fea...

This paper presents a robust speaker identification method from degraded speech signals. This method is based on the Mel-frequency cepstral coefficients (MFCCs) for feature extraction from the degraded speech signals and the wavelet transform of these signals. It is known that the MFCCs based speaker identification method is not robust enough in the presence of noise and telephone degradations....

This paper gives a brief survey on speech recognition and presents an overview for various techniques used at various stages of speech recognition systems. Researchers has been working in this research area for many years however accuracy for speech recognition still attention for variation of context, speaker’s variability, environment conditions .The development of speech recognition system r...

Speaker recognition (SR) is a dynamic biometric task. SR is a multidisplinary problem that encompasses many aspects of human speech, including speech recognition, language recognition, and speech accents. This technique makes it possible to use the speaker’s voice to verify his/her identity and provide controlled access to services. The Mel-frequency extraction method is leading approach for sp...

It is well known that human speech processing capabilities far surpass the capabilities of current automatic speech recognition and related technologies, despite very intensive research in automated speech technologies in recent decades. Indeed, since the early 1980’s, this observation has motivated the development of speech recognition feature extraction approaches that are inspired by auditor...