论文标题
端到端的演讲意图预测，以改善印地语和英语的电子商务客户支持语音机器人
End-to-End Speech to Intent Prediction to improve E-commerce Customer Support Voicebot in Hindi and English
论文作者
论文摘要
呼叫客户支持的自动化在很大程度上依赖于准确有效的语音到实体（S2I）系统。使用多组分管道构建此类系统可能会构成各种挑战，因为它们需要大的注释数据集，具有更高的延迟并具有复杂的部署。这些管道也容易复合错误。为了克服这些挑战，我们在双语环境中讨论了用于客户支持VoiceBot任务的端到端（E2E）S2I模型。我们通过利用预先训练的自动语音识别（ASR）模型来展示如何通过轻微修改和对小注释数据集进行微调来解决E2E的意图分类。实验结果表明，我们最佳的E2E模型的表现优于F1分数相对约27％的传统管道。
Automation of on-call customer support relies heavily on accurate and efficient speech-to-intent (S2I) systems. Building such systems using multi-component pipelines can pose various challenges because they require large annotated datasets, have higher latency, and have complex deployment. These pipelines are also prone to compounding errors. To overcome these challenges, we discuss an end-to-end (E2E) S2I model for customer support voicebot task in a bilingual setting. We show how we can solve E2E intent classification by leveraging a pre-trained automatic speech recognition (ASR) model with slight modification and fine-tuning on small annotated datasets. Experimental results show that our best E2E model outperforms a conventional pipeline by a relative ~27% on the F1 score.
