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...

In this paper we investigate the use of voice activity detection (VAD) for improving noise models used for cepstral domain minimum mean squared error (MMSE) filtering of noisy speech. Due to the popularity of MFCC features for speech recognition, it is useful to have VAD methods and MMSE filtering algorithms that both work in the MFCC domain. We propose a method for VAD based on the likelihood ...

Linear Discriminant Analysis (LDA) followed by a diagonalizing maximum likelihood linear transform (MLLT) applied to spliced static MFCC features yields important performance gains as compared to MFCC+dynamic features in most speech recognition tasks. It is reasonable to regularize LDA transform computation for stability. In this paper, we regularize LDA and heteroschedastic LDA transforms usin...

This work examines the utility of formant frequencies and their energies in acoustic-to-articulatory inversion. For this purpose, formant frequencies and formant spectral amplitudes are automatically estimated from audio, and are treated as observations for the purpose of estimating electromagnetic articulography (EMA) coil positions. A mixture Gaussian regression model with mel-frequency cepst...

Two systems (Statistical Trajectory Models (STM) and continuous density HMMs) utilizing three preprocessing methodologies (MFCC, RASTA and FBDYN) were evaluated on two databases, namely CTIMIT and the corresponding downsampled TIMIT. Within the bounds of the experimental setup the comparative performance analysis showed that the STM significantly outperforms the HMM system on the CTIMIT databas...

MFCCs perform well when used for clean speech recognition. However, for noisy speech the recognition rates go down. Augmenting the MFCC feature vector by dynamic features improves both discrimination and robustness of the MFCC-based recognizer. In this paper, we present an alternative para meterization based on the frequency filtering (FF) technique. By using FF, a significant improvement with ...