<p>Conventional feature extraction methods for speech emotion recognition often suffer from unidimensionality and inadequacy in capturing the full range of emotional cues, limiting their effectiveness. To address these challenges, this paper introduces a novel network model named Multi-Modal Speech Emotion Recognition Network (MMSERNet). This model leverages the power of multimodal and multiscale feature fusion to significantly enhance the accuracy of speech emotion recognition. MMSERNet is composed of three specialized sub-networks, each dedicated to the extraction of distinct feature types: cepstral coefficients, spectrogram features, and textual features. It integrates audio features derived from Mel-frequency cepstral coefficients and Mel spectrograms with textual features obtained from word vectors, thereby creating a rich, comprehensive representation of emotional content. The fusion of these diverse feature sets facilitates a robust multimodal approach to emotion recognition. Extensive empirical evaluations of the MMSERNet model on benchmark datasets such as IEMOCAP and MELD demonstrate not only significant improvements in recognition accuracy but also an efficient use of model parameters, ensuring scalability and practical applicability.</p>