Search Results for author:
Found 27 papers, 1 papers with code
Investigating Training Strategies and Model Robustness of Low-Rank Adaptation for Language Modeling in Speech Recognition
The use of low-rank adaptation (LoRA) with frozen pretrained language models (PLMs) has become increasing popular as a mainstream, resource-efficient modeling approach for memory-constrained hardware.
Towards ASR Robust Spoken Language Understanding Through In-Context Learning With Word Confusion Networks
In the realm of spoken language understanding (SLU), numerous natural language understanding (NLU) methodologies have been adapted by supplying large language models (LLMs) with transcribed speech instead of conventional written text.
Paralinguistics-Enhanced Large Language Modeling of Spoken Dialogue
Specifically, our framework serializes tasks in the order of current paralinguistic attribute prediction, response paralinguistic attribute prediction, and response text generation with autoregressive conditioning.
Discriminative Speech Recognition Rescoring with Pre-trained Language Models
In this study, we propose and explore several discriminative fine-tuning schemes for pre-trained LMs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Low-rank Adaptation of Large Language Model Rescoring for Parameter-Efficient Speech Recognition
We propose a neural language modeling system based on low-rank adaptation (LoRA) for speech recognition output rescoring.
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.
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
Robust Acoustic and Semantic Contextual Biasing in Neural Transducers for Speech Recognition
Prior approaches typically relied on subword encoders for encoding the bias phrases.
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
On-the-fly Text Retrieval for End-to-End ASR Adaptation
End-to-end speech recognition models are improved by incorporating external text sources, typically by fusion with an external language model.
USTED: Improving ASR with a Unified Speech and Text Encoder-Decoder
There has been a recent focus on training E2E ASR models that get the performance benefits of external text data without incurring the extra cost of evaluating an external language model at inference time.
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
A Likelihood Ratio based Domain Adaptation Method for E2E Models
In this work, we explore a contextual biasing approach using likelihood-ratio that leverages text data sources to adapt RNN-T model to new domains and entities.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Prompt Tuning GPT-2 language model for parameter-efficient domain adaptation of ASR systems
Automatic Speech Recognition (ASR) systems have found their use in numerous industrial applications in very diverse domains creating a need to adapt to new domains with small memory and deployment overhead.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Lattention: Lattice-attention in ASR rescoring
The rescoring model with attention to lattices achieves 4-5% relative word error rate reduction over first-pass and 6-8% with attention to both lattices and acoustic features.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Prompt-tuning in ASR systems for efficient domain-adaptation
In this work, we overcome the problem using prompt-tuning, a methodology that trains a small number of domain token embedding parameters to prime a transformer-based LM to a particular domain.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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
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%.
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.
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
Multi-task Language Modeling for Improving Speech Recognition of Rare Words
We show that our rescoring model trained with these additional tasks outperforms the baseline rescoring model, trained with only the language modeling task, by 1. 4% on a general test and by 2. 6% on a rare word test set in terms of word-error-rate relative (WERR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
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 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.
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.
