EmoAra: 一种保留情感的英语语音转录与跨语言翻译系统，结合阿拉伯语文本到语音
EmoAra: Emotion-Preserving English Speech Transcription and Cross-Lingual Translation with Arabic Text-to-Speech
AI总结：
EmoAra通过整合语音情感识别、自动语音识别、机器翻译和文本到语音技术，实现英语语音转录与阿拉伯语音输出的跨语言翻译，保留情感细微差别。
摘要：
本文介绍了EmoAra，一种端到端的情感保留跨语言语音通信管道，受银行客户服务场景启发，其中情感背景影响服务质量。EmoAra整合了语音情感识别、自动语音识别、机器翻译和文本到语音技术，以处理英语语音并生成阿拉伯语音输出，同时保留情感细微差别。该系统使用基于CNN的情感分类器，Whisper进行英语转录，经过微调的MarianMT模型用于英语到阿拉伯语翻译，以及MMS-TTS-Ara进行阿拉伯语音合成。实验报告了情感分类的F1分数为94%，翻译性能BLEU 56和BERTScore F1 88.7%，以及在银行领域翻译上的平均人工评分81%。实现和资源可在配套的GitHub仓库中获得。
英文摘要：
This work presents EmoAra, an end-to-end emotion-preserving pipeline for cross-lingual spoken communication, motivated by banking customer service where emotional context affects service quality. EmoAra integrates Speech Emotion Recognition, Automatic Speech Recognition, Machine Translation, and Text-to-Speech to process English speech and deliver an Arabic spoken output while retaining emotional nuance. The system uses a CNN-based emotion classifier, Whisper for English transcription, a fine-tuned MarianMT model for English-to-Arabic translation, and MMS-TTS-Ara for Arabic speech synthesis. Experiments report an F1-score of 94% for emotion classification, translation performance of BLEU 56 and BERTScore F1 88.7%, and an average human evaluation score of 81% on banking-domain translations. The implementation and resources are available at the accompanying GitHub repository.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
