IEEE Paper Review: Obtaining speech assets for judgement analysis on low-pass filtered emotional speech.

In this paper, different types of available assets for emotional research are reviewed. It gives brief idea on methods used to obtain the current corpus composed of high quality spontaneous speech. The paper further proposes an experiment that uses low pass filtering, on natural speech data, in order to investigate the impact cue masking has on listener’s perception of emotion in speech. The paper explains Brunswick Model For Speech. Acoustic properties are measured in reference to the encoding process and perception test on emotional speech are considered as decoding aspects. The paper further reviews existing emotional speech data. In this section, main type of data which are simulated, natural and induced vocal expressions are discussed. The fourth part gives idea about how the assets are obtained through few experiments. Two participants, placed in two isolation booths (soundproof booths), that were asked to perform a cooperative based task. Meanwhile researcher monitored, manipulated, and recorded the procedure. Later in cue masking experiment, effects of filtering on tonal quality of speech are studied. It shows that tone of the conversation can often be clearly heard even though filtering of certain frequencies are there. Thus the listeners are still able to infer vocal affect from natural speech. Then the author gives rating strategy for emotional speech. It suggest two affective scales, namely evaluation and activity on five point Liker-scale. The conclusion of this paper is that it gives research on Brunswick lens model, methods for obtaining high quality natural speech through mood induction procedure. It proposes an experiment to isolate semantic content from acoustic information by masking cues using low pass filters. The filtered and original signal are rated and compared on scale of activity and evaluation.