Abstract
A novel feature vector for speech recognition applications is proposed. These new features are obtained by generalising the two-dimensional root cepstrum (TDRC) using the mel-scaled root filter bank energies of a speech frame in the process of computing the TDRC. Results indicate superior performance over conventional techniques in an isolated word recognition problem.
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