Neha Chauhan Birla Institute of Technology, Mesra, Ranchi Abstract— Speaker Recognition is the computing task of validating a user’s claimed identity using speech characteristics. Main objective of speech recognition system is to communication with a device through our voice. Mel frequency Cepstral Coefficient (MFCC) features are combined with pitch and root mean square values and tested for im...

Feature extraction is a crucial part of many MIR tasks. Many manual-selected features such as MFCC have been applied to music processing but they are not effective for music genre classification. In this work, we present an algorithm based on spectrogram and convolutional neural network (CNN). Compared with MFCC, the spectrogram contains more details of music components such as pitch, flux, etc...

An experimental study of the application of scale-transform to improve the performance of speaker independent continuous speech recognition, is presented in this paper. Three major results are described. First, a comparison was made between the scale-transform based magnitude cepstrum coeÆcients (STCC) and mel-scale lter bank cepstrum coeÆcients (MFCC) on a telephone based connected digit recog...

The objective of this work is to study the speaker-specific nature of analytic phase of speech signals. Since computation of analytic phase suffers from phase wrapping problem, we have used its derivativethe instantaneous frequency for feature extraction. The cepstral coefficients extracted from smoothed subband instantaneous frequencies (IFCC) are used as features for speaker verification. The...

Speech Emotion Recognition (SER) is a hot research topic in the field of Human Computer Interaction (HCI). In this paper, we recognize three emotional states: happy, sad and neutral. The explored features include: energy, pitch, linear predictive spectrum coding (LPCC), Mel-frequency spectrum coefficients (MFCC), and Mel-energy spectrum dynamic coefficients (MEDC). A German Corpus (Berlin Datab...

This paper proposes evaluation of sound parameterization methods in recognizing some spoken Arabic words, namely digits from zero to nine. Each isolated spoken word is represented by a single template based on a specific recognition feature, and the recognition is based on the Euclidean distance from those templates. The performance analysis of recognition is based on four parameterization feat...

Speaker recognition is one of the most essential tasks in the signal processing which identifies a person from characteristics of voices . In this paper we accomplish speaker recognition using Mel-frequency Cepstral Coefficient (MFCC) with Weighted Vector Quantization algorithm. By using MFCC, the feature extraction process is carried out. It is one of the nonlinear cepstral coefficient functio...

This work presents an application of Fundamental Frequency (Pitch), Linear Predictive Cepstral Coefficient (LPCC) and Mel Frequency Cepstral Coefficient (MFCC) in identification of sex of the speaker in speech recognition research. The aim of this article is to compare the performance of these three methods for identification of sex of the speakers. A successful speech recognition system can he...

This paper presents a novel approach to the design of a robust speaker recognition system. A noise-free synthesised spectrum is produced from a noisy spectrum. This synthesised spectrum is used for feature extraction. From noisy speech, the pitch is extracted using arobust pitch estimation algorithm. This also helps in identifying the voiced segments of speech which are the only ones considered...

This work presents an application of Fundamental Frequency (Pitch), Linear Predictive Cepstral Coefficient (LPCC) and Mel Frequency Cepstral Coefficient (MFCC) in identification of sex of the speaker in speech recognition research. The aim of this article is to compare the performance of these three methods for identification of sex of the speakers. A successful speech recognition system can he...