与您的TV对话：使用分层循环神经网络的上下文感知语音搜索
Talking to Your TV: Context-Aware Voice Search with Hierarchical Recurrent Neural Networks
摘要：
我们解决了新的问题，导航语音查询对娱乐系统，观众互动与语音遥控器指定的节目观看。这是一个困难的问题，原因有几个：这样的查询很短，甚至比其他领域中的类似语音查询更短，这提供了更少的机会来破译用户意图。此外，潜在的语音识别错误加剧了模糊性。我们通过整合查询的单词和字符级表示以及通过建模语音搜索会话来捕获查询序列中的上下文依赖关系来解决这些挑战。两者都是用概率框架来完成的，在概率框架中，递归和前馈神经网络模块以分层的方式组织。我们从Comcast Xtag1娱乐系统上250万观众的3200万语音查询的原始数据集中提取数据来训练和测试我们的模型。我们展示了我们的混合表示和上下文感知模型的好处，它显着优于没有上下文的模型以及当前部署的产品。
英文摘要：
We tackle the novel problem of navigational voice queries posed against an entertainment system, where viewers interact with a voice-enabled remote controller to specify the program to watch. This is a difficult problem for several reasons: such queries are short, even shorter than comparable voice queries in other domains, which offers fewer opportunities for deciphering user intent. Furthermore, ambiguity is exacerbated by underlying speech recognition errors. We address these challenges by integrating word- and character-level representations of the queries and by modeling voice search sessions to capture the contextual dependencies in query sequences. Both are accomplished with a probabilistic framework in which recurrent and feedforward neural network modules are organized in a hierarchical manner. From a raw dataset of 32M voice queries from 2.5M viewers on the Comcast Xfinity X1 entertainment system, we extracted data to train and test our models. We demonstrate the benefits of our hybrid representation and context-aware model, which significantly outperforms models without context as well as the current deployed product.
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。
