26 citations · 29 across the 11 of their papers we have counts for
13 papers
Flexible Keyword Spotting based on Homogeneous Audio-Text Embedding
Kumari Nishu, Minsik Cho, Paul Dixon +1
Spotting user-defined/flexible keywords represented in text frequently uses an expensive text encoder for joint analysis with an audio encoder in an embedding space, which can suff…
Matching Latent Encoding for Audio-Text based Keyword Spotting
Kumari Nishu, Minsik Cho, Devang Naik
Using audio and text embeddings jointly for Keyword Spotting (KWS) has shown high-quality results, but the key challenge of how to semantically align two embeddings for multi-word…
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…
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…
I see what you hear: a vision-inspired method to localize words
Mohammad Samragh, Arnav Kundu, Ting-Yao Hu +5
This paper explores the possibility of using visual object detection techniques for word localization in speech data. Object detection has been thoroughly studied in the contempora…
Optimize what matters: Training DNN-HMM Keyword Spotting Model Using End Metric
Ashish Shrivastava, Arnav Kundu, Chandra Dhir +2
Deep Neural Network--Hidden Markov Model (DNN-HMM) based methods have been successfully used for many always-on keyword spotting algorithms that detect a wake word to trigger a dev…
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…
On the Role of Visual Cues in Audiovisual Speech Enhancement
Zakaria Aldeneh, Anushree Prasanna Kumar, Barry-John Theobald +4
We present an introspection of an audiovisual speech enhancement model. In particular, we focus on interpreting how a neural audiovisual speech enhancement model uses visual cues t…
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…
Leveraging Acoustic Cues and Paralinguistic Embeddings to Detect Expression from Voice
Vikramjit Mitra, Sue Booker, Erik Marchi +6
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 su…