Search Results for author:
Found 19 papers, 3 papers with code
Improving RNN Transducer Based ASR with Auxiliary Tasks
End-to-end automatic speech recognition (ASR) models with a single neural network have recently demonstrated state-of-the-art results compared to conventional hybrid speech recognizers.
Ranked #15 on
Speech Recognition
on LibriSpeech test-clean
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Emformer: Efficient Memory Transformer Based Acoustic Model For Low Latency Streaming Speech Recognition
For a low latency scenario with an average latency of 80 ms, Emformer achieves WER $3. 01\%$ on test-clean and $7. 09\%$ on test-other.
Transformer-based Acoustic Modeling for Hybrid Speech Recognition
We propose and evaluate transformer-based acoustic models (AMs) for hybrid speech recognition.
Ranked #23 on
Speech Recognition
on LibriSpeech test-other
(using extra training data)
Deja-vu: Double Feature Presentation and Iterated Loss in Deep Transformer Networks
As our motivation is to allow acoustic models to re-examine their input features in light of partial hypotheses we introduce intermediate model heads and loss function.
Training ASR models by Generation of Contextual Information
Supervised ASR models have reached unprecedented levels of accuracy, thanks in part to ever-increasing amounts of labelled training data.
Contextualizing ASR Lattice Rescoring with Hybrid Pointer Network Language Model
Videos uploaded on social media are often accompanied with textual descriptions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Streaming Transformer-based Acoustic Models Using Self-attention with Augmented Memory
The memory bankstores the embedding information for all the processed seg-ments.
Weak-Attention Suppression For Transformer Based Speech Recognition
Transformers, originally proposed for natural language processing (NLP) tasks, have recently achieved great success in automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Faster, Simpler and More Accurate Hybrid ASR Systems Using Wordpieces
In this work, we first show that on the widely used LibriSpeech benchmark, our transformer-based context-dependent connectionist temporal classification (CTC) system produces state-of-the-art results.
Ranked #17 on
Speech Recognition
on LibriSpeech test-other
(using extra training data)
Transformer in action: a comparative study of transformer-based acoustic models for large scale speech recognition applications
We compare the transformer based acoustic models with their LSTM counterparts on industrial scale tasks.
Streaming Attention-Based Models with Augmented Memory for End-to-End Speech Recognition
Attention-based models have been gaining popularity recently for their strong performance demonstrated in fields such as machine translation and automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Benchmarking LF-MMI, CTC and RNN-T Criteria for Streaming ASR
In this work, to measure the accuracy and efficiency for a latency-controlled streaming automatic speech recognition (ASR) application, we perform comprehensive evaluations on three popular training criteria: LF-MMI, CTC and RNN-T.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
On lattice-free boosted MMI training of HMM and CTC-based full-context ASR models
Hybrid automatic speech recognition (ASR) models are typically sequentially trained with CTC or LF-MMI criteria.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Improved Language Identification Through Cross-Lingual Self-Supervised Learning
Language identification greatly impacts the success of downstream tasks such as automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Accent-Robust Automatic Speech Recognition Using Supervised and Unsupervised Wav2vec Embeddings
Domain-adversarial training (DAT) and multi-task learning (MTL) are two common approaches for building accent-robust ASR models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Scaling ASR Improves Zero and Few Shot Learning
With 4. 5 million hours of English speech from 10 different sources across 120 countries and models of up to 10 billion parameters, we explore the frontiers of scale for automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Pushing the performances of ASR models on English and Spanish accents
Speech to text models tend to be trained and evaluated against a single target accent.
Short-Term Stock Price Forecasting using exogenous variables and Machine Learning Algorithms
Creating accurate predictions in the stock market has always been a significant challenge in finance.
3D-SceneDreamer: Text-Driven 3D-Consistent Scene Generation
Text-driven 3D scene generation techniques have made rapid progress in recent years.
