Search Results for author:
Found 47 papers, 1 papers with code
Federated Representation Learning for Automatic Speech Recognition
Federated Learning (FL) is a privacy-preserving paradigm, allowing edge devices to learn collaboratively without sharing data.
Personalization for BERT-based Discriminative Speech Recognition Rescoring
On a test set with personalized named entities, we show that each of these approaches improves word error rate by over 10%, against a neural rescoring baseline.
Scaling Laws for Discriminative Speech Recognition Rescoring Models
We study whether this scaling property is also applicable to second-pass rescoring, which is an important component of speech recognition systems.
Federated Self-Learning with Weak Supervision for Speech Recognition
Automatic speech recognition (ASR) models with low-footprint are increasingly being deployed on edge devices for conversational agents, which enhances privacy.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Distillation Strategies for Discriminative Speech Recognition Rescoring
We also show that the proposed distillation can reduce the WER gap between the student and the teacher by 62% upto 100%.
Streaming Speech-to-Confusion Network Speech Recognition
In interactive automatic speech recognition (ASR) systems, low-latency requirements limit the amount of search space that can be explored during decoding, particularly in end-to-end neural ASR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Personalized Predictive ASR for Latency Reduction in Voice Assistants
Streaming Automatic Speech Recognition (ASR) in voice assistants can utilize prefetching to partially hide the latency of response generation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Accelerator-Aware Training for Transducer-Based Speech Recognition
Machine learning model weights and activations are represented in full-precision during training.
Lookahead When It Matters: Adaptive Non-causal Transformers for Streaming Neural Transducers
Streaming speech recognition architectures are employed for low-latency, real-time applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Dual-Attention Neural Transducers for Efficient Wake Word Spotting in Speech Recognition
We present dual-attention neural biasing, an architecture designed to boost Wake Words (WW) recognition and improve inference time latency on speech recognition tasks.
PROCTER: PROnunciation-aware ConTextual adaptER for personalized speech recognition in neural transducers
End-to-End (E2E) automatic speech recognition (ASR) systems used in voice assistants often have difficulties recognizing infrequent words personalized to the user, such as names and places.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Using External Off-Policy Speech-To-Text Mappings in Contextual End-To-End Automated Speech Recognition
Despite improvements to the generalization performance of automated speech recognition (ASR) models, specializing ASR models for downstream tasks remains a challenging task, primarily due to reduced data availability (necessitating increased data collection), and rapidly shifting data distributions (requiring more frequent model fine-tuning).
ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scale
For end-to-end automatic speech recognition (ASR) tasks, the absence of human annotated labels along with the need for privacy preserving policies for model building makes it a daunting challenge.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Compute Cost Amortized Transformer for Streaming ASR
We present a streaming, Transformer-based end-to-end automatic speech recognition (ASR) architecture which achieves efficient neural inference through compute cost amortization.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Sub-8-Bit Quantization Aware Training for 8-Bit Neural Network Accelerator with On-Device Speech Recognition
We present a novel sub-8-bit quantization-aware training (S8BQAT) scheme for 8-bit neural network accelerators.
RescoreBERT: Discriminative Speech Recognition Rescoring with BERT
Specifically, training a bidirectional model like BERT on a discriminative objective such as minimum WER (MWER) has not been explored.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Attentive Contextual Carryover for Multi-Turn End-to-End Spoken Language Understanding
Recent years have seen significant advances in end-to-end (E2E) spoken language understanding (SLU) systems, which directly predict intents and slots from spoken audio.
Natural Language Understanding
Spoken Language Understanding
Context-Aware Transformer Transducer for Speech Recognition
We also leverage both BLSTM and pretrained BERT based models to encode contextual data and guide the network training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
FANS: Fusing ASR and NLU for on-device SLU
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.
Ranked #13 on
Spoken Language Understanding
on Fluent Speech Commands
(using extra training data)
Bifocal Neural ASR: Exploiting Keyword Spotting for Inference Optimization
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.
Amortized Neural Networks for Low-Latency Speech Recognition
We apply AmNets to the Recurrent Neural Network Transducer (RNN-T) to reduce compute cost and latency for an automatic speech recognition (ASR) task.
Ranked #51 on
Speech Recognition
on LibriSpeech test-clean
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Learning a Neural Diff for Speech Models
As more speech processing applications execute locally on edge devices, a set of resource constraints must be considered.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
On joint training with interfaces for spoken language understanding
Spoken language understanding (SLU) systems extract both text transcripts and semantics associated with intents and slots from input speech utterances.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Do You Listen with One or Two Microphones? A Unified ASR Model for Single and Multi-Channel Audio
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
Automatic Speech Recognition (ASR)
+1
Attention-based Contextual Language Model Adaptation for Speech Recognition
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
Automatic Speech Recognition (ASR)
+2
Listen with Intent: Improving Speech Recognition with Audio-to-Intent Front-End
On the other hand, a streaming system using per-frame intent posteriors as extra inputs for the RNN-T ASR system yields a 3. 33% relative WERR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Wav2vec-C: A Self-supervised Model for Speech Representation Learning
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.
Personalization Strategies for End-to-End Speech Recognition Systems
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%.
Do as I mean, not as I say: Sequence Loss Training for Spoken Language Understanding
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
Automatic Speech Recognition (ASR)
+3
Sparsification via Compressed Sensing for Automatic Speech Recognition
In the literature, such methods are referred to as sparse pruning.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Domain-aware Neural Language Models for Speech Recognition
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.
REDAT: Accent-Invariant Representation for End-to-End ASR by Domain Adversarial Training with Relabeling
Accents mismatching is a critical problem for end-to-end ASR.
Improving accuracy of rare words for RNN-Transducer through unigram shallow fusion
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
Automatic Speech Recognition (ASR)
+2
Speech To Semantics: Improve ASR and NLU Jointly via All-Neural Interfaces
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
Automatic Speech Recognition (ASR)
+4
Neural Composition: Learning to Generate from Multiple Models
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.
Streaming End-to-End Bilingual ASR Systems with Joint Language Identification
Experiments show that for English-Spanish, the bilingual joint ASR-LID architecture matches monolingual ASR and acoustic-only LID accuracies.
Neural Machine Translation for Multilingual Grapheme-to-Phoneme Conversion
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
Automatic Speech Recognition (ASR)
+3
Streaming Language Identification using Combination of Acoustic Representations and ASR Hypotheses
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.
Audio-attention discriminative language model for ASR rescoring
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
Automatic Speech Recognition (ASR)
+2
Scalable Multi Corpora Neural Language Models for ASR
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
Automatic Speech Recognition (ASR)
+1
Scalable language model adaptation for spoken dialogue systems
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.
LSTM-based Whisper Detection
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
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
Automatic Speech Recognition (ASR)
+1
Contextual Language Model Adaptation for Conversational Agents
Statistical language models (LM) play a key role in Automatic Speech Recognition (ASR) systems used by conversational agents.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding
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.
