Comparison of linear prediction cepstrum coefficients and mel-frequency cepstrum coefficients for language identification

The speech parametrization methods: linear prediction cepstrum coefficients and mel-frequency cepstrum coefficients were compared with regard to language identification accuracy in a Gaussian mixture model based language identification system. Ten different languages were used to test against a set of ten second test files. The 12th order linear prediction cepstrum coefficients with delta and accelerate coefficients resulted in the best accuracy of 60.0 percent. This has shown that information obtained from linear prediction analysis has increased the ability of discriminating different languages. It also shows that language identification performance may be increased by encompassing temporal information by including delta and acceleration features. Besides, the performance of our test system has proved the feasibility of the modeling language by a single Gaussian Mixture Model instead of using complex system such as phonetic recogniser followed by language modelling or large vocabulary continuous speech recognition system.