1 citations · 1 across the 9 of their papers we have counts for
11 papers
Adaptive Knowledge Distillation for Device-Directed Speech Detection
Hyung Gun Chi, Florian Pesce, Wonil Chang +5
Device-directed speech detection (DDSD) is a binary classification task that separates the user's queries to a voice assistant (VA) from background speech or side conversations. Th…
Device-Directed Speech Detection for Follow-up Conversations Using Large Language Models
Ognjen, Rudovic, Pranay Dighe +7
Follow-up conversations with virtual assistants (VAs) enable a user to seamlessly interact with a VA without the need to repeatedly invoke it using a keyword (after the first query…
Comparative Analysis of Personalized Voice Activity Detection Systems: Assessing Real-World Effectiveness
Satyam Kumar, Sai Srujana Buddi, Utkarsh Oggy Sarawgi +6
Voice activity detection (VAD) is a critical component in various applications such as speech recognition, speech enhancement, and hands-free communication systems. With the increa…
Streaming Anchor Loss: Augmenting Supervision with Temporal Significance
Utkarsh Oggy Sarawgi, John Berkowitz, Vineet Garg +5
Streaming neural network models for fast frame-wise responses to various speech and sensory signals are widely adopted on resource-constrained platforms. Hence, increasing the lear…
Leveraging Large Language Models for Exploiting ASR Uncertainty
Pranay Dighe, Yi Su, Shangshang Zheng +4
While large language models excel in a variety of natural language processing (NLP) tasks, to perform well on spoken language understanding (SLU) tasks, they must either rely on of…
Does Single-channel Speech Enhancement Improve Keyword Spotting Accuracy? A Case Study
Avamarie Brueggeman, Takuya Higuchi, Masood Delfarah +2
Noise robustness is a key aspect of successful speech applications. Speech enhancement (SE) has been investigated to improve automatic speech recognition accuracy; however, its eff…
Device-Directed Speech Detection: Regularization via Distillation for Weakly-Supervised Models
Vineet Garg, Ognjen Rudovic, Pranay Dighe +5
We address the problem of detecting speech directed to a device that does not contain a specific wake-word. Specifically, we focus on audio coming from a touch-based invocation. Mi…
Streaming on-device detection of device directed speech from voice and touch-based invocation
Ognjen Rudovic, Akanksha Bindal, Vineet Garg +3
When interacting with smart devices such as mobile phones or wearables, the user typically invokes a virtual assistant (VA) by saying a keyword or by pressing a button on the devic…
Streaming Transformer for Hardware Efficient Voice Trigger Detection and False Trigger Mitigation
Vineet Garg, Wonil Chang, Siddharth Sigtia +4
We present a unified and hardware efficient architecture for two stage voice trigger detection (VTD) and false trigger mitigation (FTM) tasks. Two stage VTD systems of voice assist…
Progressive Voice Trigger Detection: Accuracy vs Latency
Siddharth Sigtia, John Bridle, Hywel Richards +3
We present an architecture for voice trigger detection for virtual assistants. The main idea in this work is to exploit information in words that immediately follow the trigger phr…
Hybrid Transformer/CTC Networks for Hardware Efficient Voice Triggering
Saurabh Adya, Vineet Garg, Siddharth Sigtia +2
We consider the design of two-pass voice trigger detection systems. We focus on the networks in the second pass that are used to re-score candidate segments obtained from the first…