Fundamentals of speech recognition
Fundamentals of speech recognition
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Intelligent Processing of Stuttered Speech
Journal of Intelligent Information Systems
MSR '05 Proceedings of the 2005 international workshop on Mining software repositories
Face Recognition Using Multiple Classifiers
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
A comparison of generalized linear discriminant analysis algorithms
Pattern Recognition
Classification of audio signals using SVM and RBFNN
Expert Systems with Applications: An International Journal
Unsupervised speaker segmentation with residual phase and MFCC features
Expert Systems with Applications: An International Journal
A fall detection system using k-nearest neighbor classifier
Expert Systems with Applications: An International Journal
Proceedings of the CUBE International Information Technology Conference
Objective evaluation of speech dysfluencies using wavelet packet transform with sample entropy
Digital Signal Processing
Hi-index | 12.05 |
The goal of this paper is to discuss comparison of speech parameterization methods: Mel-Frequency Cepstrum Coefficients (MFCC) and Linear Prediction Cepstrum Coefficients (LPCC) for recognizing the stuttered events. Speech samples from UCLASS are used for our analysis. The stuttered events are identified through manual segmentation and used for feature extraction. Two simple classifiers are used for testing the proposed features. Conventional validation method is used for testing the reliability of the classifier. The experimental investigation elucidates MFCC and LPCC features which can be used for identifying the stuttered events and LPCC features were slightly outperformed than MFCC features.