To solve the problem of mismatching features in an experimental database, which is a key technique in the field of cross-corpus speech emotion recognition, an auditory attention model based on Chirplet is proposed for...To solve the problem of mismatching features in an experimental database, which is a key technique in the field of cross-corpus speech emotion recognition, an auditory attention model based on Chirplet is proposed for feature extraction.First, in order to extract the spectra features, the auditory attention model is employed for variational emotion features detection. Then, the selective attention mechanism model is proposed to extract the salient gist features which showtheir relation to the expected performance in cross-corpus testing.Furthermore, the Chirplet time-frequency atoms are introduced to the model. By forming a complete atom database, the Chirplet can improve the spectrum feature extraction including the amount of information. Samples from multiple databases have the characteristics of multiple components. Hereby, the Chirplet expands the scale of the feature vector in the timefrequency domain. Experimental results show that, compared to the traditional feature model, the proposed feature extraction approach with the prototypical classifier has significant improvement in cross-corpus speech recognition. In addition, the proposed method has better robustness to the inconsistent sources of the training set and the testing set.展开更多
Because of the excellent performance of Transformer in sequence learning tasks,such as natural language processing,an improved Transformer-like model is proposed that is suitable for speech emotion recognition tasks.T...Because of the excellent performance of Transformer in sequence learning tasks,such as natural language processing,an improved Transformer-like model is proposed that is suitable for speech emotion recognition tasks.To alleviate the prohibitive time consumption and memory footprint caused by softmax inside the multihead attention unit in Transformer,a new linear self-attention algorithm is proposed.The original exponential function is replaced by a Taylor series expansion formula.On the basis of the associative property of matrix products,the time and space complexity of softmax operation regarding the input's length is reduced from O(N2)to O(N),where N is the sequence length.Experimental results on the emotional corpora of two languages show that the proposed linear attention algorithm can achieve similar performance to the original scaled dot product attention,while the training time and memory cost are reduced by half.Furthermore,the improved model obtains more robust performance on speech emotion recognition compared with the original Transformer.展开更多
Most educated Chinese take English as an important communication tool and the language has been increasingly frequently used in all walks of life in the country. This paper is to examine the use of English emotion wor...Most educated Chinese take English as an important communication tool and the language has been increasingly frequently used in all walks of life in the country. This paper is to examine the use of English emotion words on the target group of English learners in China. The study is designed to find out the relationship between the use of emotion words and such relevant variables as language proficiency, gender, and age. The results demonstrate that the use of emotion words is significantly linked to the level of proficiency as well as gender. Age is found to have slight effect on the use of emotion words. The study also reveals that more positive emotion words are produced than negative ones in the speech. Based on the major findings, some implications and suggestions are offered： Firstly, English learners in China are expected to improve their language proficiency, particularly that of listening and speaking. Secondly, they are supposed to enhance their culture awareness of English by means of exposing themselves to as much authentic language as possible.展开更多
基金The National Natural Science Foundation of China(No.61273266,61231002,61301219,61375028)the Specialized Research Fund for the Doctoral Program of Higher Education(No.20110092130004)the Natural Science Foundation of Shandong Province(No.ZR2014FQ016)
文摘To solve the problem of mismatching features in an experimental database, which is a key technique in the field of cross-corpus speech emotion recognition, an auditory attention model based on Chirplet is proposed for feature extraction.First, in order to extract the spectra features, the auditory attention model is employed for variational emotion features detection. Then, the selective attention mechanism model is proposed to extract the salient gist features which showtheir relation to the expected performance in cross-corpus testing.Furthermore, the Chirplet time-frequency atoms are introduced to the model. By forming a complete atom database, the Chirplet can improve the spectrum feature extraction including the amount of information. Samples from multiple databases have the characteristics of multiple components. Hereby, the Chirplet expands the scale of the feature vector in the timefrequency domain. Experimental results show that, compared to the traditional feature model, the proposed feature extraction approach with the prototypical classifier has significant improvement in cross-corpus speech recognition. In addition, the proposed method has better robustness to the inconsistent sources of the training set and the testing set.
基金The National Key Research and Development Program of China(No.2020YFC2004002,2020YFC2004003)the National Natural Science Foundation of China(No.61871213,61673108,61571106).
文摘Because of the excellent performance of Transformer in sequence learning tasks,such as natural language processing,an improved Transformer-like model is proposed that is suitable for speech emotion recognition tasks.To alleviate the prohibitive time consumption and memory footprint caused by softmax inside the multihead attention unit in Transformer,a new linear self-attention algorithm is proposed.The original exponential function is replaced by a Taylor series expansion formula.On the basis of the associative property of matrix products,the time and space complexity of softmax operation regarding the input's length is reduced from O(N2)to O(N),where N is the sequence length.Experimental results on the emotional corpora of two languages show that the proposed linear attention algorithm can achieve similar performance to the original scaled dot product attention,while the training time and memory cost are reduced by half.Furthermore,the improved model obtains more robust performance on speech emotion recognition compared with the original Transformer.
文摘Most educated Chinese take English as an important communication tool and the language has been increasingly frequently used in all walks of life in the country. This paper is to examine the use of English emotion words on the target group of English learners in China. The study is designed to find out the relationship between the use of emotion words and such relevant variables as language proficiency, gender, and age. The results demonstrate that the use of emotion words is significantly linked to the level of proficiency as well as gender. Age is found to have slight effect on the use of emotion words. The study also reveals that more positive emotion words are produced than negative ones in the speech. Based on the major findings, some implications and suggestions are offered： Firstly, English learners in China are expected to improve their language proficiency, particularly that of listening and speaking. Secondly, they are supposed to enhance their culture awareness of English by means of exposing themselves to as much authentic language as possible.
