Spectro-Temporal Features for Automatic Speech Recognition using Linear Prediction in Spectral Domain

Frequency Domain Linear Prediction (FDLP) provides an efficient way to represent temporal envelopes of a signal using auto-regressive models. For the input speech signal, we use FDLP to estimate temporal trajectories of sub-band energy by applying linear prediction on the cosine transform of sub-band signals. The sub-band FDLP envelopes are used to extract spectral and temporal features for speech recognition. The spectral features are derived by integrating the temporal envelopes in short-term frames and the temporal features are formed by converting these envelopes into modulation frequency components. These features are then combined in the phoneme posterior level and used as the input features for a hybrid HMM-ANN based phoneme recognizer. The proposed spectro-temporal features provide a phoneme recognition accuracy of $69.1 \%$ (an improvement of $4.8 \%$ over the Perceptual Linear Prediction (PLP) base-line) for the TIMIT database

Text-Independent Automatic Dialect Recognition of Marathi Language using Spectro-Temporal Characteristics of Voice

Text-independent dialect recognition system is proposed in this paper for Marathi language. India is rich in language varieties. Each language in turn has its unique dialect variations. Maharashtra has Marathi as official language and for Goa it is a co-official language . In literature there are very few studies  available for Indian language recognition and then their respective dialect recognition. So research work available  for regional languages such as Marathi is extremely limited. As a part of research work, an attempt is made to generate a case study of a low resourced Marathi language dialect recognition system. The study was carried out using Marathi speech data corpus provided by Linguistic Data Consortium for Indian Language (LDC- IL). This corpus  includes four major dialects of Marathi speakers. The efficiency and performance evaluation of the explored spectral (rhythmic) and temporal features are carried out to perform classification tasks. We investigated the performance of six different classifiers; K-nearest neighbor (KNN), Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT) classifier , Stochastic Gradient Descent (SGD) classifier and Ridge Classifier (RC). Experimental results have demonstrated that the RC classifier worked well with 84.24% of accuracy for fifteen spectral and temporal features. With twelve MFCCs it has been observed that SGD  has outperformed among all classifiers with accuracy of 80.63%. For further study, a prominent feature subset as a part of dimensionality reduction  has been identified using chi square, mutual information and ANOVA-f test. In this chi-square based feature extraction method has proven to be the best over over mutual information and ANOVA f-test