6 papers · 1 filter
HEiMDaL: Highly Efficient Method for Detection and Localization of wake-words
Arnav Kundu, Mohammad Samragh Razlighi, Minsik Cho +2
Streaming keyword spotting is a widely used solution for activating voice assistants. Deep Neural Networks with Hidden Markov Model (DNN-HMM) based methods have proven to be effici…
Knowledge Transfer for Efficient On-device False Trigger Mitigation
Pranay Dighe, Erik Marchi, Srikanth Vishnubhotla +2
In this paper, we address the task of determining whether a given utterance is directed towards a voice-enabled smart-assistant device or not. An undirected utterance is termed as…
Complementary Language Model and Parallel Bi-LRNN for False Trigger Mitigation
Rishika Agarwal, Xiaochuan Niu, Pranay Dighe +3
False triggers in voice assistants are unintended invocations of the assistant, which not only degrade the user experience but may also compromise privacy. False trigger mitigation…
Detecting Emotion Primitives from Speech and their use in discerning Categorical Emotions
Vasudha Kowtha, Vikramjit Mitra, Chris Bartels +5
Emotion plays an essential role in human-to-human communication, enabling us to convey feelings such as happiness, frustration, and sincerity. While modern speech technologies rely…
Multi-task Learning for Speaker Verification and Voice Trigger Detection
Siddharth Sigtia, Erik Marchi, Sachin Kajarekar +2
Automatic speech transcription and speaker recognition are usually treated as separate tasks even though they are interdependent. In this study, we investigate training a single ne…
Lattice-based Improvements for Voice Triggering Using Graph Neural Networks
Pranay Dighe, Saurabh Adya, Nuoyu Li +6
Voice-triggered smart assistants often rely on detection of a trigger-phrase before they start listening for the user request. Mitigation of false triggers is an important aspect o…