Hybrid HMM/DTW based Speech Recognition with Kernel Adaptive Filtering Method
نویسندگان
چکیده
We have proposed new approach for the speech recognition system by applying kernel adaptive filter for speech enhancement and for the recognition, the hybrid HMM/DTW methods are used in this paper. Noise removal is very important in many applications like telephone conversation, speech recognition, etc. In the recent past, the kernel methods are showing good results for speech processing applications. The feature used in the recognition process is MFCC features. It consists of a HMM system used to train the speech features and for classification purpose used the DTW method. Experimental results show a relative improvement of recognition rate compared to the traditional methods.
منابع مشابه
Robust Speech Recognition Using Fusion Techniques and Adaptive Filtering
The study proposes an algorithm for noise cancellation by using recursive least square (RLS) and pattern recognition by using fusion method of Dynamic Time Warping (DTW) and Hidden Markov Model (HMM). Speech signals are often corrupted with background noise and the changes in signal characteristics could be fast. These issues are especially important for robust speech recognition. Robustness is...
متن کاملIsolated Words Recognition System Based on Hybrid Approach DTW/GHMM
In this paper, we present a new hybrid approach for isolated spoken word recognition using Hidden Markov Model models (HMM) combined with Dynamic time warping (DTW). HMM have been shown to be robust in spoken recognition systems. We propose to extend the HMM method by combining it with the DTW algorithm in order to combine the advantages of these two powerful pattern recognition technique. In t...
متن کاملRobust ASR using Support Vector Machines
The improved theoretical properties of Support Vector Machines with respect to other machine learning alternatives due to their max-margin training paradigm have led us to suggest them as a good technique for robust speech recognition. However, important shortcomings have had to be circumvented, the most important being the normalisation of the time duration of different realisations of the aco...
متن کاملشبکه عصبی پیچشی با پنجره‌های قابل تطبیق برای بازشناسی گفتار
Although, speech recognition systems are widely used and their accuracies are continuously increased, there is a considerable performance gap between their accuracies and human recognition ability. This is partially due to high speaker variations in speech signal. Deep neural networks are among the best tools for acoustic modeling. Recently, using hybrid deep neural network and hidden Markov mo...
متن کاملPolynomial dynamic time warping kernel support vector machines for dysarthric speech recognition with sparse training data
This paper describes a new formulation of a polynomial sequence kernel based on dynamic time warping (DTW) for support vector machine (SVM) classification of isolated words given very sparse training data. The words are uttered by dysarthric speakers who suffer from debilitating neurological conditions that make the collection of speech samples a timeconsuming and low-yield process. Data for bu...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2014