With rapid economic and technological development and the further enhancement of cultural soft power, the audience for choral art is gradually expanding. Ordinary citizens increasingly have opportunities to attend choral concerts, participate in choral competitions, and personally experience the charm of choral art. This study establishes a virtual choral training platform using virtual reality technology, creates a multimodal scene model, builds a comprehensive data transmission mechanism, and employs dot matrix technology to accurately simulate facial expressions. Voice analysis technology extracts vocal parameters of choral singing, including first formant, third formant, fundamental frequency, vocal range, and fundamental frequency perturbation. Subsequently, a Resnet-GRU-based choral singer recognition model is proposed, incorporating the embedded attention mechanism module SEnet to effectively enhance recognition performance. Results indicate that vocal patterns in singing exhibit greater discriminative power than those in natural speech. Compared to phoneme-based recognition, Resnet-GRU-based recognition achieves average accuracy improvements of 36.1%, 36.7%, and 29.8% for choral vocalization and retroflex consonants, respectively. Upon implementation, this method effectively enhances performers’ emotional engagement while amplifying their expressive impact.
