## 1 Introduction  
The ability to correctly identify the emotion of a speaker is an indispensable aspect of our daily lives, especially in situations where the speaker's facial expressions and gestures are not visible (when only the voice is observed). Thus, to obtain high-quality communication, it is necessary to clarify the mechanism of vocal emotion recognition. Previous studies on vocal emotion recognition were based on acoustic features and sound patterns. For example, one study investigated the acoustic features on the basis of the source-filter model (fundamental frequency (F0), speaking rate, intensity, duration, etc.) to illustrate that the confusion patterns were close to those found for listener-judges [1]. To model the expressive perceptions of speech, [PERSON] and [PERSON] proposed a three-layered model with acoustic features in the bottom layer [2]. The results confirmed significant relationships between expressive speech, semantic primitives, and acoustic features. The acoustic features required for the vocal emotion recognition of some listeners cannot be satisfied, such as for listeners who use cochlear implants (CIs). CIs have proved to be very useful artificial organs for listeners with hearing loss because CIs enable users to achieve speech intelligibility at a high level in a clean auditory environment. In a study on vocal emotion perception involving listeners with CIs, acoustic features had difficulty accounting for the human response from the CI listeners [3]. The probable reason is that CIs provide the temporal amplitude envelope (TAE) information as a primary cue but with the loss of spectro-temporal detail, which is needed to support the perception of harmonic pitch. As a result, pitch-dominant aspects of speech, such as vocal emotion and lexical tone recognition, are weakly transmitted by CIs [4]. For the normal cochlea, speech is decomposed into narrowband signals, each with a relatively slowly varying TAE and temporal fine structure (TFS). As illustrated in previous research, certain hearing problems, such as hearing loss with age, affect the processing of TFS information but do not affect the processing of TAE information [5]. The above insights have revealed the insufficiency of acoustic features, as they leave out the important contributions of TAE to the vocal emotion recognition of listeners. They also highlight the need to clarify the important role of TAE information and its cues in vocal emotion recognition. It is beneficial to improve the vocal-emotion-recognition quality of CI listeners and to elucidate the vocal-emotion-recognition mechanism of normal hearing. To clarify how TAE information works in the perception of speech information, both modern physiological [6] and psychological [7] evidence suggest the existence of a modulation filterbank in the auditory system, which is used to analyze the modulation-frequency components of the TAE. In the peripheral hearing system, the cochlea converts speech signals from the middle ear into the corresponding neural signals [5]. The signal processes in the peripheral auditory system can be computationally modeled as a band-pass filterbank, envelope extraction, and amplitude compression [8; 9]. On the basis of this peripheral auditory system, previous studies have proved that TAE information of speech signals plays an important role in vocal emotion recognition [10; 11; 12; 13]. [PERSON] et al. used auditory-based modulation analysis to extract the modulation spectral features (MSFs) from emotional speech and investigated the contribution of MSFs to vocal emotion recognition using noise-vocoded speech (NVS) in a clean environment with no noise and no reverberation [14; 15]. The results indicated that there were high similarities between MSFs and the results from vocal-emotion-recognition experiments, suggesting that the MSFs of TAE are useful in accounting for the perceptual processing of vocal-emotion with NVS.