Comparison of Different Speech Feature Extraction Techniques with and without Wavelet Transform to Kannada Speech Recognition

نویسندگان

  • M. A. Anusuya
  • S. K. Katti
چکیده

Pre-processing of speech signals is considered a crucial step in the development of a robust and efficient speech or speaker recognition system. This paper deals with different speech processing techniques and the recognition accuracy with respect to wavelet transforms. It is shown that by applying wavelet transform to the conventional methods the signal recognition accuracy will be increased by using discrete wavelet transforms and the wavelet packets for clean and noisy speech signals respectively. Results presented in the tabular form, shows the advantage of pre-processing the signals with wavelet techniques gives good results over conventional methods.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Comparison of Time-Frequency Feature Extraction Techniques for Environmental Sound Recognition

This paper is the continuation of previously published work in which we have been analysing different methods – traditionally used in speech recognition – for their suitability to be applied to Environmental Sound Recognition. While current research devotes much effort to speech and speaker recognition, Environmental Sound Recognition is an area where little research has been reported. Despite ...

متن کامل

Mel Frequency Discrete Wavelet Coefficients for Kannada Speech Recognition using PCA

In this paper, a new scheme for recognition of isolated words in kannada Language speech, based on the Discrete Wavelet Transform(DWT) and PCA has been proposed. First, the DWT of the speech is computed and then MFCC coefficients are calculated. For this, Principal Component Analysis procedure is applied for speech recognition. This paper also presents the comparative results with respect to th...

متن کامل

New Feature Extraction Techniques for Marathi Digit Recognition

In this paper a new efficient feature extraction methods for speech recognition have been proposed. The features are obtained from Cepstral Mean Normalized reduced order Linear Predictive Coding (LPC) coefficients derived from the speech frames decomposed using Discrete Wavelet Transform (DWT). In the literature it is assumed that the speech frame of size 10 msec to 30 msec is stationary, howev...

متن کامل

A New Method for Speech Enhancement Based on Incoherent Model Learning in Wavelet Transform Domain

Quality of speech signal significantly reduces in the presence of environmental noise signals and leads to the imperfect performance of hearing aid devices, automatic speech recognition systems, and mobile phones. In this paper, the single channel speech enhancement of the corrupted signals by the additive noise signals is considered. A dictionary-based algorithm is proposed to train the speech...

متن کامل

A Wavelet Based Approach for Speaker Identification from Degraded Speech

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....

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2011