Emotional Speech Conversion Using Deep Neural Networks
Recently, the study of Voice Conversion (VC) is being widely attracted attention in the field of speech processing. This technology can be widely applied in various application domains. For instances, speech enhancement, emotion conversion, speaking assistance and other applications [1] are related to VC. Therefore, the need for this type of technology in various fields has continued to propel related research forward each year. Many statistical approaches have been proposed for spectral conversion during the last. Among these approaches, a Gaussian Mixture Model (GMM) is widely used. However, the features trained by GMM are usually low dimensional features which may lost some important spectral details during making the speech spectra. The high dimensional features, such as Mel Frequency Cepstral Coefficents MFCCs which are widely used in automatic speech and speaker recognition, are not compatible with GMM. There are also some approaches to construct non linear mapping relationships, such as using artificial neural networks (ANNs) to train the mapping dictionaries between source and target features, using deep belief networks (DBNs) to achieve non linear deep transformation [2] These models improve the conversion of spectrum features. Nevertheless, most of the related works in respect to VC focus on the conversion of spectrum features, yet the seldom of those focus on F0 conversion, because F0 cannot be processed by a high dimensional model neural networks (NNs) well. But F0 is one of the most important parameters for representing emotional speech.For emotional voice conversion, some prosody features, such as pitch variables and speaking rate have already been analyzed. There are also some works using a GMM based VC technique to change the emotional voice [3] As above mentioned, recently acoustic voice conversion usually uses the non linear suitable models (NNs, CRBMs, DBNs, RTDBNs) to convert the spectral features, it is difficult to use the GMM to deal with F0 made by these frameworks. To solve these problems, we proposed a new approach.