# A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments
## 2 Related Work  
The performance of speaker identification is exceptionally high in a neutral talking environment compared to other talking environments such as emotional and stressful [16; 17; 18]. Conversely, its performance is lower in the case of a stressful talking environment [19; 20; 21]. A neutral talking environment is the kind of talking environment whereby the speaker utters the speech without any stressful or emotional talking condition. On the other hand, a stressful talking environment is different from neutral talking conditions in the sense that the speakers deliver their speech under stressful talking conditions, such as shouting or speaking loudly, and quickly. The authors in [19] investigated \"talker-stress-induced intra-word variability\", and the algorithm meant to counter these systematic changes, based on \"hidden Markov models (HMMs)\" as classifiers that had been trained with the help of speech indicationsunder different types of talking conditions. By using the hypothesis-driven compensation technique, the error rate was reduced from 13.9% to 6.2%. [PERSON] and [PERSON] [20] focused on studying speaker recognition in stressed conditions with the aim of improving the decline in performance usually observed in these conditions. They made use of four types of stressed conditions of the SUSAS database [22; 23], including neutral, angry, Lombard, and question conditions. The study revealed that speech uttered by speakers under angry conditions exhibited the lowest speaker identification performance [20]. The average speaker identification rate using the SUSAS database (stressed condition) was about 57%. [PERSON] and [PERSON] [24] examined five different vocal modes, including whispered, soft, neutral, loud, and shouted, with the aim of studying various aspects of speech. They intended to identify distinguishing features of speech modes. The average accuracy rate was about 96%. [PERSON] [25] tried to learn through the use of data with sequential dynamics and put forward infinite-order HMM models that were based on the assumption that first-order Markovian dependencies existed among the successive label values denoted by \(y\). The models that were designed were better than other techniques, for a couple of reasons. First, extended and complex temporal dependencies can be captured by these models. Second, margin maximization paradigms are employed for performing model training in these models, ultimately leading to a convex optimization design [25]. The highest average accuracy obtained was 71.68% for the iMMS model. In another work, [PERSON] et al. (2020) [26] proposed a method using the deep time-delay Markov network (DTMN) to predict emotions by studying earlier emotional states. The novel approach has been evaluated on the English SUSAS database. They concluded in that study that the proposed DTMN outperformed the baseline systems. TDNN-4 was the optimal temporal context for predicting the emotional state of 8.31% PER. There are very few research studies that focus on the spoken Arabic language as speech [27; 28]. This is mainly because Arabic speech databases are quite limited with reference to the speaker recognition area. There are four main regional dialects of the Arabic language. They include \"Egyptian, Levantine (e.g., Jordan), North African (e.g., Tunisian), and Gulf Arabic (e.g., Emirati)\" [29]. There is currently no published work dealing with speaker identification in stressful talking environments using the RBFNN-CNN model. In this paper, a significant contribution has been made for enhancing speaker identification within stressful talking environments. For this purpose, two speech databases were applied for model testing of speaker identification within stressful talking environments. \"Speech Under Simulated and Actual Stress (SUSAS)\" is the first database that has been recorded using stressful and neutral talking [22].