This paper proposes localized affine invariant features (LAIFs) for speaker-independent automatic speech recognition. The LAIFs can be calculated directly from data sequences. As speaker variations can be approximated well by affine transform in a cepstral space, the LAIFs can provide robust features with respect to those variations. This fact inspires us to expect that the use of the LAIFs sho...

Voice control is an important way of controlling mobile devices; however, using it remains a challenge for dysarthric patients. Currently, there are many approaches, such as automatic speech recognition (ASR) systems, being used to help patients devices. However, the large computation power requirement ASR system increases implementation costs. To alleviate this problem, study proposed convolut...

Speaker verification using limited data is always a challenge for practical implementation as an application. An analysis on speaker verification studies for an i-vector based method using Mel-Frequency Cepstral Coefficient (MFCC) feature shows that the performance drops drastically as the duration of test data is reduced. This decrease in performance is due to insufficient phonetic coverage wh...

The inconvenience operation of EEG P300 or functional magnetic resonance imaging (FMRI) will be overcome, when the deceptive information can be effectively detected from speech signal analysis. In this paper, the fractional Mel cepstral coefficient (FrCC) is proposed as the speech character for deception detection. The different fractional order can reveal various personalities of the speakers....

In this paper, a feature extraction method that is robust to additive background noise is proposed for automatic speech recognition. Since the background noise corrupts the autocorrelation coefficients of the speech signal mostly at the lowertime lags, while the higher-lag autocorrelation coefficients are least affected, this method discards the lower-lag autocorrelation coefficients and uses o...

Speaker verification in real-world applications sometimes deals with limited duration of enrollment and/or test data. MFCC-based i-vector systems have defined the state-of-the-art for speaker verification, but it is well known that they are less effective with short utterances. To address this issue, we propose a method to leverage the speaker specificity and stationarity of subglottal acoustic...

Music genre classification has a significant role in information retrieval for the organization of growing collections music. It is challenging to classify music with reliable accuracy. Many methods have utilized handcrafted features identify unique patterns but are still unable determine original characteristics. Comparatively, using deep learning models been shown be dynamic and effective. Am...

Mel Frequency Cepstral Coefficient (MFCC) features are widely used as acoustic features for speech recognition as well as speaker recognition. In MFCC feature representation, the Mel frequency scale is used to get a high resolution in low frequency region, and a low resolution in high frequency region. This kind of processing is good for obtaining stable phonetic information, but not suitable f...

Digital processing of speech signal and voice recognition algorithm is very important for fast and accurate automatic voice recognition technology. The voice is a signal of infinite information. A direct analysis and synthesizing the complex voice signal is due to too much information contained in the signal. Therefore the digital signal processes such as Feature Extraction and Feature Matching...