View publication

Voice assistants aim to fulfill user requests by choosing the best intent from multiple options generated by its Automated Speech Recognition and Natural Language Understanding sub-systems. However, voice assistants do not always produce the expected results. This can happen because voice assistants choose from ambiguous intents. User-specific or domain-specific contextual information can reduce the ambiguity of the user request. Additionally, the user information-state can be leveraged to understand how relevant or executable a specific intent is for a user request. In this work, we propose a novel energy based model for the intent ranking task, where we learn an affinity metric and model the trade-off between extracted meaning from speech utterances and relevance aspects of the intent. Furthermore, we present a Multisource Denoising Autoencoder based pretraining that is capable of learning fused representations of data from multiple sources. We empirically show our approach outperforms existing state of the art methods by reducing the error-rate by 3.8 percent, which in turn reduces ambiguity and eliminates undesired dead ends leading to better user experience. Finally, we evaluate the robustness of our algorithm on the intent ranking task and show our algorithm improves the robustness by 33.3 percent.

Related readings and updates.

Noise-robust Named Entity Understanding for Virtual Assistants

Named Entity Understanding (NEU) plays an essential role in interactions between users and voice assistants, since successfully identifying entities and correctly linking them to their standard forms is crucial to understanding the user's intent. NEU is a challenging task in voice assistants due to the ambiguous nature of natural language and because noise introduced by speech transcription and user errors occur frequently in spoken natural…
See paper details

Leveraging Acoustic Cues and Paralinguistic Embeddings to Detect Expression from Voice

Millions of people reach out to digital assistants such as Siri every day, asking for information, making phone calls, seeking assistance, and much more. The expectation is that such assistants should understand the intent of the user’s query. Detecting the intent of a query from a short, isolated utterance is a difficult task. Intent cannot always be obtained from speech-recognized transcriptions. A transcription-driven approach can interpret…
See paper details