Speaker Trait Enhancement for Cochlear Implant Users: A Case Study for Speaker Emotion Perception

Despite significant progress in areas such as speech recognition, cochlear implant users still experience challenges related to identifying various speaker traits such as gender, age, emotion, accent, etc. In this study, we focus on emotion as one trait. We propose the use of emotion intensity conversion to perceptually enhance emotional speech with the goal of improving speech emotion recognition for cochlear implant users. To this end, we utilize a parallel speech dataset containing emotion and intensity labels to perform conversion from normal to high intensity emotional speech. A non-negative matrix factorization method is integrated to perform emotion intensity conversion via spectral mapping. We evaluate our emotional speech enhancement using a support vector machine model for emotion recognition. In addition, we perform an emotional speech recognition listener experiment with normal hearing listeners using vocoded audio. It is suggested that such enhancement will benefit speaker trait perception for cochlear implant users.