no code implementations • 23 Oct 2023 • Gautam Krishna, Sameer Dharur, Oggi Rudovic, Pranay Dighe, Saurabh Adya, Ahmed Hussen Abdelaziz, Ahmed H Tewfik
Device-directed speech detection (DDSD) is the binary classification task of distinguishing between queries directed at a voice assistant versus side conversation or background speech.
no code implementations • 9 Sep 2023 • Pranay Dighe, Yi Su, Shangshang Zheng, Yunshu Liu, Vineet Garg, Xiaochuan Niu, Ahmed Tewfik
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 off-the-shelf automatic speech recognition (ASR) systems for transcription, or be equipped with an in-built speech modality.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +7
no code implementations • 21 Oct 2022 • Pranay Dighe, Prateeth Nayak, Oggi Rudovic, Erik Marchi, Xiaochuan Niu, Ahmed Tewfik
Accurate prediction of the user intent to interact with a voice assistant (VA) on a device (e. g. on the phone) is critical for achieving naturalistic, engaging, and privacy-centric interactions with the VA. To this end, we present a novel approach to predict the user's intent (the user speaking to the device or not) directly from acoustic and textual information encoded at subword tokens which are obtained via an end-to-end ASR model.
no code implementations • 30 Mar 2022 • Vineet Garg, Ognjen Rudovic, Pranay Dighe, Ahmed H. Abdelaziz, Erik Marchi, Saurabh Adya, Chandra Dhir, Ahmed Tewfik
We also show that the ensemble of the LatticeRNN and acoustic-distilled models brings further accuracy improvement of 20%.
no code implementations • 9 Oct 2021 • Ognjen Rudovic, Akanksha Bindal, Vineet Garg, Pramod Simha, Pranay Dighe, Sachin Kajarekar
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 device.
no code implementations • 14 May 2021 • Vineet Garg, Wonil Chang, Siddharth Sigtia, Saurabh Adya, Pramod Simha, Pranay Dighe, Chandra Dhir
We propose a streaming transformer (TF) encoder architecture, which progressively processes incoming audio chunks and maintains audio context to perform both VTD and FTM tasks using only acoustic features.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 20 Oct 2020 • Pranay Dighe, Erik Marchi, Srikanth Vishnubhotla, Sachin Kajarekar, Devang Naik
But in case of a false trigger, transcribing the audio using ASR itself is strongly undesirable.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 18 Aug 2020 • Rishika Agarwal, Xiaochuan Niu, Pranay Dighe, Srikanth Vishnubhotla, Sameer Badaskar, Devang Naik
In this paper, we propose a novel solution to the FTM problem by introducing a parallel ASR decoding process with a special language model trained from "out-of-domain" data sources.
no code implementations • 25 Jan 2020 • Pranay Dighe, Saurabh Adya, Nuoyu Li, Srikanth Vishnubhotla, Devang Naik, Adithya Sagar, Ying Ma, Stephen Pulman, Jason Williams
A pure trigger-phrase detector model doesn't fully utilize the intent of the user speech whereas by using the complete decoding lattice of user audio, we can effectively mitigate speech not intended for the smart assistant.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 29 Aug 2017 • Pranay Dighe, Afsaneh Asaei, Hervé Bourlard
We propose an information theoretic framework for quantitative assessment of acoustic modeling for hidden Markov model (HMM) based automatic speech recognition (ASR).
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 18 Oct 2016 • Pranay Dighe, Afsaneh Asaei, Herve Bourlard
Conventional deep neural networks (DNN) for speech acoustic modeling rely on Gaussian mixture models (GMM) and hidden Markov model (HMM) to obtain binary class labels as the targets for DNN training.
no code implementations • 22 Jan 2016 • Pranay Dighe, Gil Luyet, Afsaneh Asaei, Herve Bourlard
We propose to model the acoustic space of deep neural network (DNN) class-conditional posterior probabilities as a union of low-dimensional subspaces.