SVM-based speech endpoint detection using contextual speech features
نویسندگان
چکیده
Shown is an effective speech endpoint detection algorithm using a trained support vector machine (SVM) and a feature vector including contextual information speech features. With this and other innovations the proposed algorithm yields high discrimination and reports significant improvements over standard methods and algorithms defining the decision rule in terms of averaged subband speech features.
منابع مشابه
Improving of Feature Selection in Speech Emotion Recognition Based-on Hybrid Evolutionary Algorithms
One of the important issues in speech emotion recognizing is selecting of appropriate feature sets in order to improve the detection rate and classification accuracy. In last studies researchers tried to select the appropriate features for classification by using the selecting and reducing the space of features methods, such as the Fisher and PCA. In this research, a hybrid evolutionary algorit...
متن کاملAutomatic error detection for unit selection speech synthesis using log likelihood ratio based SVM classifier
This paper proposes a method to detect the errors in synthetic speech of a unit selection speech synthesis system automatically using log likelihood ratio and support vector machine (SVM). For SVM training, a set of synthetic speech are firstly generated by a given speech synthesis system and their synthetic errors are labeled by manually annotating the segments that sound unnatural. Then, two ...
متن کاملRobot Arm Performing Writing through Speech Recognition Using Dynamic Time Warping Algorithm
This paper aims to develop a writing robot by recognizing the speech signal from the user. The robot arm constructed mainly for the disabled people who can’t perform writing on their own. Here, dynamic time warping (DTW) algorithm is used to recognize the speech signal from the user. The action performed by the robot arm in the environment is done by reducing the redundancy which frequently fac...
متن کاملSpeech/non-speech segments detection based on chaotic and prosodic features
Every speech recognition system contains a speech/nonspeech detection stage. Detected speech sequences are only passed through the speech recognition stage later on. In a noisy environment, non-speech segments can be an important source of error. In this work, we introduce a new speech/nonspeech detection system based on fractal dimension and prosodic features plus the common used MFCC features...
متن کاملRobust entropy-based endpoint detection for speech recognition in noisy environments
This paper presents an entropy-based algorithm for accurate and robust endpoint detection for speech recognition under noisy environments. Instead of using the conventional energy-based features, the spectral entropy is developed to identify the speech segments accurately. Experimental results show that this algorithm outperforms the energy-based algorithms in both detection accuracy and recogn...
متن کامل