Dziri Voicebot: 面向阿尔及利亚方言的端到端低资源语音到语音对话系统
Dziri Voicebot: An End-to-End Low-Resource Speech-to-Speech Conversational System for Algerian Dialect
AI总结：
针对阿尔及利亚方言的低资源挑战，提出模块化流水线集成ASR、NLU、RAG和TTS的端到端语音对话系统，在电信领域数据集上微调模型，实现低词错误率、高意图分类和实体识别性能。
摘要：
自动语音和语言技术仍然严重偏向高资源语言，限制了它们在方言和低资源环境（如阿尔及利亚方言）中的适用性。这种语言还面临额外挑战，包括缺乏标准化正字法、频繁与法语进行语码转换以及标注语音资源的稀缺。本文解决了为阿尔及利亚方言构建完整语音到语音对话系统的问题。我们提出了一个模块化流水线，在统一架构中集成自动语音识别、自然语言理解、检索增强生成和文本到语音合成。本工作是我们之前关于阿尔及利亚方言对话系统Bechiri和Lanasri [2026]的延续，将其从基于文本的对话建模扩展到完整的基于语音的交互。我们为电信领域构建了专门的ASR、NLU和TTS数据集，并为每个组件微调预训练模型。ASR系统基于Whisper的适配构建，而NLU模块结合了基于Transformer的嵌入与面向任务的对话框架。神经TTS系统在新收集的方言语料库上训练，以实现语音响应生成。实验结果显示所有组件均表现强劲，包括ASR的低词错误率、NLU的高意图分类和实体识别分数以及稳定的语音合成质量。所提出的系统为阿尔及利亚方言的端到端对话建模提供了可复现的基线。
英文摘要：
Automatic speech and language technologies are still heavily biased toward high-resource languages, limiting their applicability to dialectal and low-resource settings such as Algerian Dialect. This language presents additional challenges including lack of standardized orthography, frequent codeswitching with French, and scarcity of annotated speech resources. This paper addresses the problem of building a complete speech-to-speech conversational system for Algerian Dialect. We propose a modular pipeline integrating automatic speech recognition, natural language understanding, retrieval-augmented generation, and text-to-speech synthesis within a unified architecture. This work is the continuation of our previous work on Algerian dialectal conversational systems Bechiri and Lanasri [2026], extending it from text-based dialogue modeling to full speech-based interaction. We constructed dedicated datasets for ASR, NLU, and TTS in the telecom domain and fine-tune pretrained models for each component. The ASR system is built on Whisper-based adaptation, while the NLU module combines transformer-based embeddings with a task-oriented dialogue framework. A neural TTS system is trained on a newly collected dialectal corpus to enable spoken response generation. Experimental results show strong performance across all components, including low word error rate for ASR, high intent classification and entity recognition scores for NLU, and stable speech synthesis quality. The proposed system provides a reproducible baseline for end-to-end conversational modeling in Algerian Dialect.
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。
