State-of-the-art automatic speaker recognition systems use mel-frequency cepstral coefficients (MFCC) features to describe the spectral properties of speakers. In forensic phonetics, the long-term average spectrum (LTAS) has been used for the same purpose. LTAS provides an intuitive graphical representation which can be used to visualize and quantify speaker differences. However, few studies ha...

In this paper, a voiced-unvoiced measure is used as acoustic feature for continuous speech recognition. The voiced-unvoiced measure was combined with the standard Mel Frequency Cepstral Coefficients (MFCC) using linear discriminant analysis (LDA) to choose the most relevant features. Experiments were performed on the SieTill (German digit strings recorded over telephone line) and on the SPINE (...

Nowadays synthetic voice is frequently used to defraud a biometric access system which are speaker recognition based. This paper presents synthetic speech detection in automatic speaker verification system (ASV) for the purpose of spoof detection. Feature extraction is done by canonical Mel Frequency Cepstral Coefficients (MFCC) algorithm and classification of natural and synthetic voice are do...

This thesis is concerned with reconstructing an intelligible time-domain speech signal from speech recognition features, such as Mel-frequency cepstral coefficients (MFCCs), in a distributed speech recognition(DSR) environment. The initial reconstruction methods in this thesis require, in addition to MFCC vectors, fundamental frequency and voicing information. In the later parts of the thesis t...

This paper describes the implementation of two isolated digit recognition techniques and is a comparison between the algorithms implemented. Any digit recognition comprises of mainly two stages feature extraction and similarity evaluation. Here, two feature extraction techniques, namely linear predictive cepstral coefficients (LPCC) and mel frequency cepstral coefficients (MFCC) are implemented...

In this paper, we present a novel method to extract noise-robust speech feature representation in speech recognition. This method employs the algorithm of linear predictive coding (LPC) on the feature time series of mel-frequency cepstral coefficients (MFCC). The resulting linear predictive version of the feature time 國立暨南國際大學電機工程學系 Department of Electrical Engineering, National Chi Nan Univers...

The detection of human and spoofed (synthetic/converted) speech has started to receive more attention. In this study, relative phase information extracted from a Fourier spectrum is used to detect human and spoofed speech. Because original/natural phase information is almost entirely lost in spoofed speech using current synthesis/conversion techniques, a modified group delay based feature, the ...

Lots of work has been done on speech and speaker recognition. Many technologies were developed for the analysis of speech waveforms. Musical instrument recognition is an important aspect of music information retrieval system. In this paper we analyzed features for musical instruments recognition and a brief study on music similarity. Music similarity search is done by using mel-frequency cepstr...

Making no claim of being exhaustive, a review of the most popular MFCC (Mel Frequency Cepstral Coefficients) implementations is made. These differ mainly in the particular approximation of the nonlinear pitch perception of human, the filter bank design, and the compression of the filter bank output. Then, a comparative evaluation of the presented implementations is performed on the task of text...

The mobile robot is a system that can move according to function and task. An example an industrial taking objects using remote control system. Robots controlled manual are generally carried out on robots. Many researchers have developed methods, such as image or sound-based control. In this study, the was applied in unobstructed room voice commands. methods used Mel-Frequency Cepstral Coeffici...