Discrete Speech Recognition Using a Hausdorff Based Metric - An Automatic Word-Based Speech Recognition Approach

In this work we provide an automatic speaker-independent word-based discrete speech recognition approach. Our proposed method consist of several processing levels. First, an word-based audio segmentation is performed, then a feature extraction is applied on the obtained segments. The speech feature vectors are computed using a delta delta mel cepstral vocal sound analysis. Then, a minimum distance supervised classifier is proposed. Because of the different dimensions of the speech feature vectors, we create a Hausdorff-based nonlinear metric to measure the distance between them.