S. Karpagavalli ¹, E. Chandra ²

Affiliations
1 Department of Computer Science, PSGR Krishnammal College for Women, Coimbatore - 641004, Tamil Nadu, IN
2 Department of Computer Science, Bharathiar University, Coimbatore - 641046, Tamil Nadu, IN

Abstract

Most of the speech recognition systems are designed based on the sub-word unit phoneme which is the basic sound unit of a language. In the proposed work, a novel hierarchical approach based phoneme classification task has been carried out to reduce time complexity and search space. Hierarchical classification of set of Tamil phonemes has been done in three levels. Phoneme boundaries of the given speech utterance are identified using Spectral Transition Measure (STM) and phonemes are separated. Mel-Frequency Cepstral Coefficients (MFCC) are extracted for each phoneme represented by 9 frames including the contextual frames of corresponding phoneme. In each hierarchical level, different number of models is built using Support Vector Machine (SVM) for classifying each phoneme group/phoneme. It is observed from the results that in hierarchical approach phoneme group recognition rate at level 1 and 2 has greatly improved compared to flat classification model. Complexity of search space is significantly reduced at level 2 and level 3 contrasts to flat phoneme classification model. Hierarchical phoneme classifier can be very well employed in phoneme recognition task which is useful in applications such as spoken term detection, out-ofvocabulary detection, named entity recognition, spoken document retrieval.

Keywords

Mel-Frequency Cepstral Coefficients, Spectral Transition Measure

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K. Manikandan ¹, E. Chandra ²

Affiliations
1 Department of Computer Science, PSG College of Arts and Science, Coimbatore – 641015, Tamil Nadu, IN
2 Department of Computer Science, Bharathiar University, Coimbatore – 641046, IN

Abstract

Objectives: The proposed speaker identification using a novel prosody with fuzzy based hierarchical decision tree approach and is used to modifying the limitations of existing traditional methods. It improves the performance of speaker identification in given population under noisy environments. Methods/Statistics: The key idea of this approach is to achieve an enhanced efficiency speaker cluster group using prosody features with fuzzy clustering at each level to construct the hierarchical decision tree. At each level, a speaker at belong to same groups are constructed. The proposed method has novelty of prosody as pitch and loudness with fuzzy clustering are used. Findings: An experimental result shows that the proposed model using prosody features outperforms the efficiency of speaker accuracy rate of 93.75 when compare to vocal source accuracy rate of 81.25 under noisy environments. Applications: Gender and age identification, banking and smart voice based technology operation.

Keywords

Fuzzy Clustering, Large Population Speaker Identification, Prosody Feature Extraction, Prosody with Fuzzy based Hierarchical Decision Tree.

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