Dialect Identification using Chroma-Spectral Shape Features with Ensemble Technique. (November 2021) - Chittaragi, Nagaratna B.; Koolagudi, Shashidhar G.
Highlights: Chroma based musical features are explored to capture rhythmic and music related features among dialects of a Kannada a Dravidian language. Spectral statistical characteristics are computed from short term spectra in terms of spectral shape features. A combination of Chroma and Spectral shape features is evaluated on the Kannada and English dialects. The single classifier based support vector machine (SVM) and ensemble based SVM algorithms are employed for evaluation of the derived combination of features. The system is found to be more suitable for forensic applications for speaker profiling tasks. The proposed method has shown sustained improvement over the baseline system under limited data and noisy environment. Abstract: The present work proposes a text-independent dialect identification system. Generally, dialects of a language exhibit varying pronunciation styles followed in a specific geographical region. In this paper, chroma features familiar with music-related systems are employed for identification of dialects. In addition, eight significant spectral shape related features from short term spectra are computed and combined along with chroma features and named as chroma-spectral shape features. Chroma features try to aggregate spectral information and attempt to encapsulate the evidential variations, concerning timbre, correlated melody, rhythmic, and intonation patterns found prominently among dialects of few languages. The effectiveness of the proposed features and approach is evaluated on five prominent Kannada dialects spoken in Karnataka, India and internationally known standard Intonation Variation in English (IViE) dataset with nine British English dialects. Discriminative models such as, single classifier based Support Vector Machine (SVM) and ensemble based support vector machines (ESVM) are employed for classification. The proposed features have shown better performance over state-of-the-art i-vector features on both datasets. The highest recognition performance of 95.6% and 97.52% are achieved in the cases of Kannada and IViE dialect datasets respectively using ESVM. Proposed features have also demonstrated robust performance with small sized (limited data) audio clips even in noisy conditions.