Search Results for author: Ariya Rastrow

Found 32 papers, 1 papers with code

FANS: Fusing ASR and NLU for on-device SLU

no code implementations31 Oct 2021 Martin Radfar, Athanasios Mouchtaris, Siegfried Kunzmann, Ariya Rastrow

In this paper, we introduce FANS, a new end-to-end SLU model that fuses an ASR audio encoder to a multi-task NLU decoder to infer the intent, slot tags, and slot values directly from a given input audio, obviating the need for transcription.

Spoken Language Understanding

Bifocal Neural ASR: Exploiting Keyword Spotting for Inference Optimization

no code implementations3 Aug 2021 Jonathan Macoskey, Grant P. Strimel, Ariya Rastrow

We present Bifocal RNN-T, a new variant of the Recurrent Neural Network Transducer (RNN-T) architecture designed for improved inference time latency on speech recognition tasks.

Frame Inference Optimization +4

Amortized Neural Networks for Low-Latency Speech Recognition

no code implementations3 Aug 2021 Jonathan Macoskey, Grant P. Strimel, Jinru Su, Ariya Rastrow

We apply AmNets to the Recurrent Neural Network Transducer (RNN-T) to reduce compute cost and latency for an automatic speech recognition (ASR) task.

Automatic Speech Recognition Frame

Learning a Neural Diff for Speech Models

no code implementations3 Aug 2021 Jonathan Macoskey, Grant P. Strimel, Ariya Rastrow

As more speech processing applications execute locally on edge devices, a set of resource constraints must be considered.

Automatic Speech Recognition Model Compression +1

End-to-End Spoken Language Understanding using RNN-Transducer ASR

no code implementations30 Jun 2021 Anirudh Raju, Gautam Tiwari, Milind Rao, Pranav Dheram, Bryan Anderson, Zhe Zhang, Bach Bui, Ariya Rastrow

We propose an end-to-end trained spoken language understanding (SLU) system that extracts transcripts, intents and slots from an input speech utterance.

Automatic Speech Recognition Natural Language Understanding +1

Do You Listen with One or Two Microphones? A Unified ASR Model for Single and Multi-Channel Audio

no code implementations4 Jun 2021 Gokce Keskin, Minhua Wu, Brian King, Harish Mallidi, Yang Gao, Jasha Droppo, Ariya Rastrow, Roland Maas

An ASR model that operates on both primary and auxiliary data can achieve better accuracy compared to a primary-only solution; and a model that can serve both primary-only (PO) and primary-plus-auxiliary (PPA) modes is highly desirable.

Automatic Speech Recognition

Attention-based Contextual Language Model Adaptation for Speech Recognition

1 code implementation Findings (ACL) 2021 Richard Diehl Martinez, Scott Novotney, Ivan Bulyko, Ariya Rastrow, Andreas Stolcke, Ankur Gandhe

When applied to a large de-identified dataset of utterances collected by a popular voice assistant platform, our method reduces perplexity by 7. 0% relative over a standard LM that does not incorporate contextual information.

Automatic Speech Recognition voice assistant

Wav2vec-C: A Self-supervised Model for Speech Representation Learning

no code implementations9 Mar 2021 Samik Sadhu, Di He, Che-Wei Huang, Sri Harish Mallidi, Minhua Wu, Ariya Rastrow, Andreas Stolcke, Jasha Droppo, Roland Maas

However, the quantization process is regularized by an additional consistency network that learns to reconstruct the input features to the wav2vec 2. 0 network from the quantized representations in a way similar to a VQ-VAE model.

Quantization Representation Learning +1

Personalization Strategies for End-to-End Speech Recognition Systems

no code implementations15 Feb 2021 Aditya Gourav, Linda Liu, Ankur Gandhe, Yile Gu, Guitang Lan, Xiangyang Huang, Shashank Kalmane, Gautam Tiwari, Denis Filimonov, Ariya Rastrow, Andreas Stolcke, Ivan Bulyko

We also describe a novel second-pass de-biasing approach: used in conjunction with a first-pass shallow fusion that optimizes on oracle WER, we can achieve an additional 14% improvement on personalized content recognition, and even improve accuracy for the general use case by up to 2. 5%.

14 Speech Recognition

Do as I mean, not as I say: Sequence Loss Training for Spoken Language Understanding

no code implementations12 Feb 2021 Milind Rao, Pranav Dheram, Gautam Tiwari, Anirudh Raju, Jasha Droppo, Ariya Rastrow, Andreas Stolcke

Spoken language understanding (SLU) systems extract transcriptions, as well as semantics of intent or named entities from speech, and are essential components of voice activated systems.

Automatic Speech Recognition Natural Language Understanding +1

Domain-aware Neural Language Models for Speech Recognition

no code implementations5 Jan 2021 Linda Liu, Yile Gu, Aditya Gourav, Ankur Gandhe, Shashank Kalmane, Denis Filimonov, Ariya Rastrow, Ivan Bulyko

As voice assistants become more ubiquitous, they are increasingly expected to support and perform well on a wide variety of use-cases across different domains.

Domain Adaptation Speech Recognition

Improving accuracy of rare words for RNN-Transducer through unigram shallow fusion

no code implementations30 Nov 2020 Vijay Ravi, Yile Gu, Ankur Gandhe, Ariya Rastrow, Linda Liu, Denis Filimonov, Scott Novotney, Ivan Bulyko

We show that this simple method can improve performance on rare words by 3. 7% WER relative without degradation on general test set, and the improvement from USF is additive to any additional language model based rescoring.

Automatic Speech Recognition

Speech To Semantics: Improve ASR and NLU Jointly via All-Neural Interfaces

no code implementations14 Aug 2020 Milind Rao, Anirudh Raju, Pranav Dheram, Bach Bui, Ariya Rastrow

Finally, we contrast these methods to a jointly trained end-to-end joint SLU model, consisting of ASR and NLU subsystems which are connected by a neural network based interface instead of text, that produces transcripts as well as NLU interpretation.

Automatic Speech Recognition Natural Language Understanding +1

Neural Composition: Learning to Generate from Multiple Models

no code implementations10 Jul 2020 Denis Filimonov, Ravi Teja Gadde, Ariya Rastrow

Decomposing models into multiple components is critically important in many applications such as language modeling (LM) as it enables adapting individual components separately and biasing of some components to the user's personal preferences.

Language Modelling

Neural Machine Translation for Multilingual Grapheme-to-Phoneme Conversion

no code implementations25 Jun 2020 Alex Sokolov, Tracy Rohlin, Ariya Rastrow

Grapheme-to-phoneme (G2P) models are a key component in Automatic Speech Recognition (ASR) systems, such as the ASR system in Alexa, as they are used to generate pronunciations for out-of-vocabulary words that do not exist in the pronunciation lexicons (mappings like "e c h o" to "E k oU").

Automatic Speech Recognition Machine Translation +1

Streaming Language Identification using Combination of Acoustic Representations and ASR Hypotheses

no code implementations1 Jun 2020 Chander Chandak, Zeynab Raeesy, Ariya Rastrow, Yuzong Liu, Xiangyang Huang, Siyu Wang, Dong Kwon Joo, Roland Maas

A common approach to solve multilingual speech recognition is to run multiple monolingual ASR systems in parallel and rely on a language identification (LID) component that detects the input language.

Language Identification Speech Recognition

Audio-attention discriminative language model for ASR rescoring

no code implementations6 Dec 2019 Ankur Gandhe, Ariya Rastrow

In this work, we propose to combine the benefits of end-to-end approaches with a conventional system using an attention-based discriminative language model that learns to rescore the output of a first-pass ASR system.

Automatic Speech Recognition

Scalable Multi Corpora Neural Language Models for ASR

no code implementations2 Jul 2019 Anirudh Raju, Denis Filimonov, Gautam Tiwari, Guitang Lan, Ariya Rastrow

Neural language models (NLM) have been shown to outperform conventional n-gram language models by a substantial margin in Automatic Speech Recognition (ASR) and other tasks.

Automatic Speech Recognition

Scalable language model adaptation for spoken dialogue systems

no code implementations11 Dec 2018 Ankur Gandhe, Ariya Rastrow, Bjorn Hoffmeister

New application intents and interaction types are released for these systems over time, imposing challenges to adapt the LMs since the existing training data is no longer sufficient to model the future user interactions.

Speech Recognition Spoken Dialogue Systems

LSTM-based Whisper Detection

no code implementations20 Sep 2018 Zeynab Raeesy, Kellen Gillespie, Zhenpei Yang, Chengyuan Ma, Thomas Drugman, Jiacheng Gu, Roland Maas, Ariya Rastrow, Björn Hoffmeister

We prove that, with enough data, the LSTM model is indeed as capable of learning whisper characteristics from LFBE features alone compared to a simpler MLP model that uses both LFBE and features engineered for separating whisper and normal speech.

Device-directed Utterance Detection

no code implementations7 Aug 2018 Sri Harish Mallidi, Roland Maas, Kyle Goehner, Ariya Rastrow, Spyros Matsoukas, Björn Hoffmeister

In this work, we propose a classifier for distinguishing device-directed queries from background speech in the context of interactions with voice assistants.

Automatic Speech Recognition

Contextual Language Model Adaptation for Conversational Agents

no code implementations26 Jun 2018 Anirudh Raju, Behnam Hedayatnia, Linda Liu, Ankur Gandhe, Chandra Khatri, Angeliki Metallinou, Anu Venkatesh, Ariya Rastrow

Statistical language models (LM) play a key role in Automatic Speech Recognition (ASR) systems used by conversational agents.

Automatic Speech Recognition

Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding

no code implementations1 Nov 2017 Anjishnu Kumar, Arpit Gupta, Julian Chan, Sam Tucker, Bjorn Hoffmeister, Markus Dreyer, Stanislav Peshterliev, Ankur Gandhe, Denis Filiminov, Ariya Rastrow, Christian Monson, Agnika Kumar

This paper presents the design of the machine learning architecture that underlies the Alexa Skills Kit (ASK) a large scale Spoken Language Understanding (SLU) Software Development Kit (SDK) that enables developers to extend the capabilities of Amazon's virtual assistant, Alexa.

Spoken Language Understanding

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