Two-stage dimensional emotion recognition by fusing predictions of acoustic and text networks using SVM

Automatic speech emotion recognition (SER) by a computer is a critical component for more natural human-machine interaction. As in human-human interaction, the capability to perceive emotion correctly is essential to take further steps in a particular situation. One issue in SER is whether it is necessary to combine acoustic features with other data such as facial expressions, text, and motion capture. This research proposes to combine acoustic and text information by applying a late-fusion approach consisting of two steps. First, acoustic and text features are trained separately in deep learning systems. Second, the prediction results from the deep learning systems are fed into a support vector machine (SVM) to predict the final regression score. Furthermore, the task in this research is dimensional emotion modeling because it can enable a deeper analysis of affective states. Experimental results show that this two-stage, late-fusion approach, obtains higher performance than that of any one-stage processing, with a linear correlation from one-stage to two-stage processing. This late-fusion approach improves previous early fusion results measured in concordance correlation coefficients score.

Speak Like a Professional: Increasing Speech Intelligibility by Mimicking Professional Announcer Voice with Voice Conversion

In most of practical scenarios, the announcement system must deliver speech messages in a noisy environment, in which the background noise cannot be cancelled out. The local noise reduces speech intelligibility and increases listening effort of the listener, hence hamper the effectiveness of announcement system. There has been reported that voices of professional announcers are clearer and more comprehensive than that of non-expert speakers in noisy environment. This finding suggests that the speech intelligibility might be related to the speaking style of professional announcer, which can be adapted using voice conversion method. Motivated by this idea, this paper proposes a speech intelligibility enhancement in noisy environment by applying voice conversion method on non-professional voice. We discovered that the professional announcers and non-professional speakers are clusterized into different clusters on the speaker embedding plane. This implies that the speech intelligibility can be controlled as an independent feature of speaker individuality. To examine the advantage of converted voice in noisy environment, we experimented using test words masked in pink noise at different SNR levels. The results of objective and subjective evaluations confirm that the speech intelligibility of converted voice is higher than that of original voice in low SNR conditions.