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The emotive manifestation of a news anchor ought not to be arbitrary, but rather meticulously crafted and refined to elicit a more coherent emotional response. Hence, the identification of the appropriate emotional disposition during news broadcasts constitutes a meritorious domain of inquiry. To address concerns related to imprecise extraction of speech features and intricate detection of emotional states in broadcasting, this study presents an innovative Chinese pronunciation system predicated on speech recognition. The GA-SVM algorithm is employed to ascertain the endpoint of input Chinese speech signals and extract emotional feature parameters. For the recognition of the emotional temperament in broadcast speech, a PNN model is utilized to execute the decoding process of Viterbi in HMM. Experimental findings evince that the SVM optimized by GA attains a robust classification effect across diverse test samples. Moreover, the PNN-HMM model exhibits a noteworthy capacity to withstand noise during Chinese speech extraction, thereby enabling accurate discernment of the emotional characteristics of the speech under examination. Additionally, this research furnishes a valuable point of reference for the application of intelligent classification technology to audio information.

Application of PNN-HMM model based on emotion-speech combination in broadcast intelligent communication analysis