Search Results for author:
Found 26 papers, 1 papers with code
Multi-teacher Distillation for Multilingual Spelling Correction
Accurate spelling correction is a critical step in modern search interfaces, especially in an era of mobile devices and speech-to-text interfaces.
Retrieve and Copy: Scaling ASR Personalization to Large Catalogs
Personalization of automatic speech recognition (ASR) models is a widely studied topic because of its many practical applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Generalized zero-shot audio-to-intent classification
Our multimodal training approach improves the accuracy of zero-shot intent classification on unseen intents of SLURP by 2. 75% and 18. 2% for the SLURP and internal goal-oriented dialog datasets, respectively, compared to audio-only training.
Multilingual Contextual Adapters To Improve Custom Word Recognition In Low-resource Languages
A recent approach explores Contextual Adapters, wherein an attention-based biasing model for CTC is used to improve the recognition of custom entities.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Don't Stop Self-Supervision: Accent Adaptation of Speech Representations via Residual Adapters
Speech representations learned in a self-supervised fashion from massive unlabeled speech corpora have been adapted successfully toward several downstream tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
DCTX-Conformer: Dynamic context carry-over for low latency unified streaming and non-streaming Conformer ASR
To address this issue, we propose the integration of a novel dynamic contextual carry-over mechanism in a state-of-the-art (SOTA) unified ASR system.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Masked Audio Text Encoders are Effective Multi-Modal Rescorers
Masked Language Models (MLMs) have proven to be effective for second-pass rescoring in Automatic Speech Recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Mask The Bias: Improving Domain-Adaptive Generalization of CTC-based ASR with Internal Language Model Estimation
Internal language model estimation (ILME) has been proposed to mitigate this bias for autoregressive models such as attention-based encoder-decoder and RNN-T.
Dynamic Chunk Convolution for Unified Streaming and Non-Streaming Conformer ASR
Overall, our proposed model reduces the degradation of the streaming mode over the non-streaming full-contextual model from 41. 7% and 45. 7% to 16. 7% and 26. 2% on the LibriSpeech test-clean and test-other datasets respectively, while improving by a relative 15. 5% WER over the previous state-of-the-art unified model.
Rethinking the Role of Scale for In-Context Learning: An Interpretability-based Case Study at 66 Billion Scale
Using a 66 billion parameter language model (OPT-66B) across a diverse set of 14 downstream tasks, we find this is indeed the case: $\sim$70% of attention heads and $\sim$20% of feed forward networks can be removed with minimal decline in task performance.
Device Directedness with Contextual Cues for Spoken Dialog Systems
In this work, we define barge-in verification as a supervised learning task where audio-only information is used to classify user spoken dialogue into true and false barge-ins.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Towards Personalization of CTC Speech Recognition Models with Contextual Adapters and Adaptive Boosting
End-to-end speech recognition models trained using joint Connectionist Temporal Classification (CTC)-Attention loss have gained popularity recently.
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
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
Remember the context! ASR slot error correction through memorization
Accurate recognition of slot values such as domain specific words or named entities by automatic speech recognition (ASR) systems forms the core of the Goal-oriented Dialogue Systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
ASR Adaptation for E-commerce Chatbots using Cross-Utterance Context and Multi-Task Language Modeling
In this paper, we investigate various techniques to improve contextualization, content word robustness and domain adaptation of a Transformer-XL neural language model (NLM) to rescore ASR N-best hypotheses.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Adapting Long Context NLM for ASR Rescoring in Conversational Agents
Neural Language Models (NLM), when trained and evaluated with context spanning multiple utterances, have been shown to consistently outperform both conventional n-gram language models and NLMs that use limited context.
Contextual Biasing of Language Models for Speech Recognition in Goal-Oriented Conversational Agents
In this paper, we explore different ways to incorporate context into a LSTM based NLM in order to model long range dependencies and improve speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Best of Both Worlds: Robust Accented Speech Recognition with Adversarial Transfer Learning
Training deep neural networks for automatic speech recognition (ASR) requires large amounts of transcribed speech.
Neural Inverse Text Normalization
We propose an efficient and robust neural solution for ITN leveraging transformer based seq2seq models and FST-based text normalization techniques for data preparation.
Towards Semi-Supervised Semantics Understanding from Speech
Much recent work on Spoken Language Understanding (SLU) falls short in at least one of three ways: models were trained on oracle text input and neglected the Automatics Speech Recognition (ASR) outputs, models were trained to predict only intents without the slot values, or models were trained on a large amount of in-house data.
Multimodal Semi-supervised Learning Framework for Punctuation Prediction in Conversational Speech
Experiments conducted on the Fisher corpus show that our proposed approach achieves ~6-9% and ~3-4% absolute improvement (F1 score) over the baseline BLSTM model on reference transcripts and ASR outputs respectively.
Robust Prediction of Punctuation and Truecasing for Medical ASR
We also present techniques for domain and task specific adaptation by fine-tuning masked language models with medical domain data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Zero-Shot Reinforcement Learning with Deep Attention Convolutional Neural Networks
Simulation-to-simulation and simulation-to-real world transfer of neural network models have been a difficult problem.
SAVEHR: Self Attention Vector Representations for EHR based Personalized Chronic Disease Onset Prediction and Interpretability
Chronic disease progression is emerging as an important area of investment for healthcare providers.
Robustness to Capitalization Errors in Named Entity Recognition
Robustness to capitalization errors is a highly desirable characteristic of named entity recognizers, yet we find standard models for the task are surprisingly brittle to such noise.
