86 citations · 220 across the 13 of their papers we have counts for
15 papers
SELMA: A Speech-Enabled Language Model for Virtual Assistant Interactions
Dominik Wagner, Alexander Churchill, Siddharth Sigtia +1
In this work, we present and evaluate SELMA, a Speech-Enabled Language Model for virtual Assistant interactions that integrates audio and text as inputs to a Large Language Model (…
A Multimodal Approach to Device-Directed Speech Detection with Large Language Models
Dominik Wagner, Alexander Churchill, Siddharth Sigtia +4
Interactions with virtual assistants typically start with a predefined trigger phrase followed by the user command. To make interactions with the assistant more intuitive, we explo…
Multimodal Data and Resource Efficient Device-Directed Speech Detection with Large Foundation Models
Dominik Wagner, Alexander Churchill, Siddharth Sigtia +4
Interactions with virtual assistants typically start with a trigger phrase followed by a command. In this work, we explore the possibility of making these interactions more natural…
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…
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…
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…
Multi-task Learning for Voice Trigger Detection
Siddharth Sigtia, Pascal Clark, Rob Haynes +2
We describe the design of a voice trigger detection system for smart speakers. In this study, we address two major challenges. The first is that the detectors are deployed in compl…
Automatic Environmental Sound Recognition: Performance versus Computational Cost
Siddharth Sigtia, Adam M. Stark, Sacha Krstulovic +1
In the context of the Internet of Things (IoT), sound sensing applications are required to run on embedded platforms where notions of product pricing and form factor impose hard co…
Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging
Yong Xu, Qiang Huang, Wenwu Wang +4
Environmental audio tagging aims to predict only the presence or absence of certain acoustic events in the interested acoustic scene. In this paper we make contributions to audio t…
Learning to Generate Genotypes with Neural Networks
Alexander W. Churchill, Siddharth Sigtia, Chrisantha Fernando
Neural networks and evolutionary computation have a rich intertwined history. They most commonly appear together when an evolutionary algorithm optimises the parameters and topolog…
An End-to-End Neural Network for Polyphonic Piano Music Transcription
Siddharth Sigtia, Emmanouil Benetos, Simon Dixon
We present a supervised neural network model for polyphonic piano music transcription. The architecture of the proposed model is analogous to speech recognition systems and compris…
A Hybrid Recurrent Neural Network For Music Transcription
Siddharth Sigtia, Emmanouil Benetos, Nicolas Boulanger-Lewandowski +3
We investigate the problem of incorporating higher-level symbolic score-like information into Automatic Music Transcription (AMT) systems to improve their performance. We use recur…
A Denoising Autoencoder that Guides Stochastic Search
Alexander W. Churchill, Siddharth Sigtia, Chrisantha Fernando
An algorithm is described that adaptively learns a non-linear mutation distribution. It works by training a denoising autoencoder (DA) online at each generation of a genetic algori…