Role of different order ranges of autocorrelation sequence on the performance of speech recognition

Authors:
Poonam Bansal;Amita Dev;Shail Bala Jain
Affiliations:
Department of Computer Science and Engineering, Amity School of Engineering and Technology, Guru Gobind Singh Indraprastha University, New Delhi, India;Department of Computer Science and Engineering, Ambedkar Institute of Technology, Guru Gobind Singh Indraprastha University, New Delhi, India;Department of Electronics and Communication Engineering, IGIT, Guru Gobind Singh Indraprastha University, New Delhi, India
Venue:
WSEAS TRANSACTIONS on SYSTEMS
Year:
2010

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Assessment for automatic speech recognition II: NOISEX-92: a database and an experiment to study the effect of additive noise on speech recognition systems

Speech Communication - Special issue on speech processing in adverse conditions

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Abstract

In this paper, cepstral features derived from the Differentiated Relative Higher Order Autocorrelation Sequence Spectrum (DRHOASS) are proposed for improving the robustness of a speech recognizer in the presence of background noise. Proposed method is analyzed and compared in terms of the autocorrelation coefficients they employ with the traditional feature extraction methods based on Linear Pediction (LP) analysis. LP- based techniques used are Linear Predictive Cepstral Coefficients (LPCC), Short-Time Modified Coherence (SMC) and the One-Sided Autocorrelation Linear Prediction Coefficient (OSALPC). We evaluate the speech recognition performance of the proposed features on the Hindi isolated-word task and show that the proposed features show better recognition performance than the features derived from the robust liner prediction based methods for noisy speech.