Spectral and textural features for automatic classification of fricatives

Classification of unvoiced fricatives is an important stage in applications such as spoken term detection and audio-video synchronization, and in technologies for the hearing impaired. Due to their acoustic similarity, extraction of multiple features and construction of high-dimensional feature vectors are required for successful classification of these phonemes. In this study two dimensionality reduction algorithms, namely, t-distributed Stochastic Neighbor Embedding (t-SNE) and Sequential Forward Floating Selection (SFFS) were used to obtain a compact representation of the data. A classification stage (kNN or SVM) was then applied, in which we compared the identification rates between the original feature vector and the low-dimensional representation. A total of 1000 unvoiced fricatives (/s/ /sh/ /f/ and /th/) derived from the TIMIT speech database, containing 25000 short frames of 8 ms each, were used for the evaluation. We show that representing the data by a feature vector with as few as 3 dimensions, yields a classification rate of almost 90% which outperforms most of the results obtained in previous studies.