Abstract
The end-to-end approach provides better performance in speech recognition compared to the traditional hidden Markov model-deep neural network (HMM-DNN)-based approach, but still shows poor performance in abnormal speech, especially emotional speech. The optimal solution is to build an acoustic model suitable for emotional speech recognition using only emotional speech data for each emotion, but it is impossible because it is difficult to collect sufficient amount of emotional speech data for each emotion. In this study, we propose a method to improve the emotional speech recognition performance by using the knowledge distillation technique that was originally introduced to decrease computational intensity of deep learning-based approaches by reducing the number of model parameters. In addition to its use as model compression, we employ this technique for model adaptation to emotional speech. The proposed method builds a basic model (referred to as a teacher model) with a number of model parameters using an amount of normal speech data, and then constructs a target model (referred to as a student model) with fewer model parameters using a small amount of emotional speech data (i.e., adaptation data). Since the student model is built with emotional speech data, it is expected to reflect the emotional characteristics of each emotion well. In the emotional speech recognition experiment, the student model maintained recognition performance regardless of the number of model parameters, whereas the teacher model degraded performance significantly as the number of parameters decreased, showing performance degradation of about 10% in word error rate. This result demonstrates that the student model serves as an acoustic model suitable for emotional speech recognition even though it does not require much emotional speech data.