5 citations · 18 across the 16 of their papers we have counts for
18 papers
Logbook: Extremely Long-form Audio Event Understanding
Kwanghee Choi, Suwon Shon, Dmitriy Serdyuk +7
Audio benchmarks are built around short, pre-segmented clips, limiting model design to brief inputs or fixed vocabularies. To close this gap, we introduce Logbook, a benchmark for…
MMW: Side Talk Rejection Multi-Microphone Whisper on Smart Glasses
Yang Liu, Li Wan, Yiteng Huang +5
Smart glasses are increasingly positioned as the next-generation interface for ubiquitous access to large language models (LLMs). Nevertheless, achieving reliable interaction in re…
Multimodal Large Language Models with Fusion Low Rank Adaptation for Device Directed Speech Detection
Shruti Palaskar, Oggi Rudovic, Sameer Dharur +7
Although Large Language Models (LLMs) have shown promise for human-like conversations, they are primarily pre-trained on text data. Incorporating audio or video improves performanc…
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…
Modality Dropout for Multimodal Device Directed Speech Detection using Verbal and Non-Verbal Features
Gautam Krishna, Sameer Dharur, Oggi Rudovic +4
Device-directed speech detection (DDSD) is the binary classification task of distinguishing between queries directed at a voice assistant versus side conversation or background spe…
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…
eDKM: An Efficient and Accurate Train-time Weight Clustering for Large Language Models
Minsik Cho, Keivan A. Vahid, Qichen Fu +5
Since Large Language Models or LLMs have demonstrated high-quality performance on many complex language tasks, there is a great interest in bringing these LLMs to mobile devices fo…
Efficient Multimodal Neural Networks for Trigger-less Voice Assistants
Sai Srujana Buddi, Utkarsh Oggy Sarawgi, Tashweena Heeramun +4
The adoption of multimodal interactions by Voice Assistants (VAs) is growing rapidly to enhance human-computer interactions. Smartwatches have now incorporated trigger-less methods…
PDP: Parameter-free Differentiable Pruning is All You Need
Minsik Cho, Saurabh Adya, Devang Naik
DNN pruning is a popular way to reduce the size of a model, improve the inference latency, and minimize the power consumption on DNN accelerators. However, existing approaches migh…
R2 Loss: Range Restriction Loss for Model Compression and Quantization
Arnav Kundu, Chungkuk Yoo, Srijan Mishra +2
Model quantization and compression is widely used techniques to reduce usage of computing resource at inference time. While state-of-the-art works have been achieved reasonable acc…
Improving Voice Trigger Detection with Metric Learning
Prateeth Nayak, Takuya Higuchi, Anmol Gupta +9
Voice trigger detection is an important task, which enables activating a voice assistant when a target user speaks a keyword phrase. A detector is typically trained on speech data…
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…
DKM: Differentiable K-Means Clustering Layer for Neural Network Compression
Minsik Cho, Keivan A. Vahid, Saurabh Adya +1
Deep neural network (DNN) model compression for efficient on-device inference is becoming increasingly important to reduce memory requirements and keep user data on-device. To this…
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…
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…
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…
Nonlinear Conjugate Gradients For Scaling Synchronous Distributed DNN Training
Saurabh Adya, Vinay Palakkode, Oncel Tuzel
Nonlinear conjugate gradient (NLCG) based optimizers have shown superior loss convergence properties compared to gradient descent based optimizers for traditional optimization prob…
Democratizing Production-Scale Distributed Deep Learning
Minghuang Ma, Hadi Pouransari, Daniel Chao +5
The interest and demand for training deep neural networks have been experiencing rapid growth, spanning a wide range of applications in both academia and industry. However, trainin…