A novel visualized sound description, called sound dendrogram is proposed to make manual annotation easier when building large speech corpora. It is a lattice structure built from a group of "seed regions" and throu...A novel visualized sound description, called sound dendrogram is proposed to make manual annotation easier when building large speech corpora. It is a lattice structure built from a group of "seed regions" and through an iterative procedure of mergence. A simple but reliable extraction method of "seed regkms" and advanced distance metric are adopted to construct the sound dendrogram, so that it can present speech's structure character ranging from coarse to fine in a visualized way. Tests show that all phonemic boundaries are contained in the lattice structure of sound dendrogram and very easy to identify. Sound dendrogram can be a powerful assistant tool during the process of speech corporals manual annotation.展开更多
Automatic Speech Emotion Recognition(SER)is used to recognize emotion from speech automatically.Speech Emotion recognition is working well in a laboratory environment but real-time emotion recognition has been influen...Automatic Speech Emotion Recognition(SER)is used to recognize emotion from speech automatically.Speech Emotion recognition is working well in a laboratory environment but real-time emotion recognition has been influenced by the variations in gender,age,the cultural and acoustical background of the speaker.The acoustical resemblance between emotional expressions further increases the complexity of recognition.Many recent research works are concentrated to address these effects individually.Instead of addressing every influencing attribute individually,we would like to design a system,which reduces the effect that arises on any factor.We propose a two-level Hierarchical classifier named Interpreter of responses(IR).Thefirst level of IR has been realized using Support Vector Machine(SVM)and Gaussian Mixer Model(GMM)classifiers.In the second level of IR,a discriminative SVM classifier has been trained and tested with meta information offirst-level classifiers along with the input acoustical feature vector which is used in primary classifiers.To train the system with a corpus of versatile nature,an integrated emotion corpus has been composed using emotion samples of 5 speech corpora,namely;EMO-DB,IITKGP-SESC,SAVEE Corpus,Spanish emotion corpus,CMU's Woogle corpus.The hierarchical classifier has been trained and tested using MFCC and Low-Level Descriptors(LLD).The empirical analysis shows that the proposed classifier outperforms the traditional classifiers.The proposed ensemble design is very generic and can be adapted even when the number and nature of features change.Thefirst-level classifiers GMM or SVM may be replaced with any other learning algorithm.展开更多
Most of the information in digital world is accessible to few who can read or understand a particular language. The speech corpus acquisition is an essential part of all spoken technology systems. The quality and the ...Most of the information in digital world is accessible to few who can read or understand a particular language. The speech corpus acquisition is an essential part of all spoken technology systems. The quality and the volume of speech data in corpus directly affect the accuracy of the system. However, there are a lot of scopes to develop speech technology system using Hindi language which is spoken primarily in India. To achieve such an ambitious goal, the collection of standard database is a prerequisite. This paper summarizes the Hindi corpus and lexical resources being developed by various organizations across the country.展开更多
文摘A novel visualized sound description, called sound dendrogram is proposed to make manual annotation easier when building large speech corpora. It is a lattice structure built from a group of "seed regions" and through an iterative procedure of mergence. A simple but reliable extraction method of "seed regkms" and advanced distance metric are adopted to construct the sound dendrogram, so that it can present speech's structure character ranging from coarse to fine in a visualized way. Tests show that all phonemic boundaries are contained in the lattice structure of sound dendrogram and very easy to identify. Sound dendrogram can be a powerful assistant tool during the process of speech corporals manual annotation.
文摘Automatic Speech Emotion Recognition(SER)is used to recognize emotion from speech automatically.Speech Emotion recognition is working well in a laboratory environment but real-time emotion recognition has been influenced by the variations in gender,age,the cultural and acoustical background of the speaker.The acoustical resemblance between emotional expressions further increases the complexity of recognition.Many recent research works are concentrated to address these effects individually.Instead of addressing every influencing attribute individually,we would like to design a system,which reduces the effect that arises on any factor.We propose a two-level Hierarchical classifier named Interpreter of responses(IR).Thefirst level of IR has been realized using Support Vector Machine(SVM)and Gaussian Mixer Model(GMM)classifiers.In the second level of IR,a discriminative SVM classifier has been trained and tested with meta information offirst-level classifiers along with the input acoustical feature vector which is used in primary classifiers.To train the system with a corpus of versatile nature,an integrated emotion corpus has been composed using emotion samples of 5 speech corpora,namely;EMO-DB,IITKGP-SESC,SAVEE Corpus,Spanish emotion corpus,CMU's Woogle corpus.The hierarchical classifier has been trained and tested using MFCC and Low-Level Descriptors(LLD).The empirical analysis shows that the proposed classifier outperforms the traditional classifiers.The proposed ensemble design is very generic and can be adapted even when the number and nature of features change.Thefirst-level classifiers GMM or SVM may be replaced with any other learning algorithm.
文摘Most of the information in digital world is accessible to few who can read or understand a particular language. The speech corpus acquisition is an essential part of all spoken technology systems. The quality and the volume of speech data in corpus directly affect the accuracy of the system. However, there are a lot of scopes to develop speech technology system using Hindi language which is spoken primarily in India. To achieve such an ambitious goal, the collection of standard database is a prerequisite. This paper summarizes the Hindi corpus and lexical resources being developed by various organizations across the country.
基金国家自然科学基金(the National Natural Science Foundation of China under Grant No.60475022) 山西省自然科学基金(the Natural Science Foundation of Shanxi Province of China under Grant No.20041041)山西省回国留学人员基金(No.2002004)。