Robust speaker-independent word recognition using static, dynamic and acceleration features: experiments with Lombard and noisy speech

Speaker-independent recognition of Lombard and noisy speech by a recognizer trained with normal speech is discussed. Speech was represented by static, dynamic (first difference), and acceleration (second difference) features. Strong interaction was found between these temporal features, the frequency differentiation due to cepstral weighting, and the degree of smoothing in the spectral analysis. When combined with the other features, acceleration raised recognition rates for Lombard or noisy input speech. Dynamic and acceleration features were found to perform much better than the static feature for noisy Lombard speech. This suggests that an algorithm which excludes the static feature in high ambient noise is desirable