Search Results for author:
Found 17 papers, 8 papers with code
NVIDIA NeMo Offline Speech Translation Systems for IWSLT 2022
Our cascade system consists of 1) Conformer RNN-T automatic speech recognition model, 2) punctuation-capitalization model based on pre-trained T5 encoder, 3) ensemble of Transformer neural machine translation models fine-tuned on TED talks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Stateful Conformer with Cache-based Inference for Streaming Automatic Speech Recognition
We also showed that training a model with multiple latencies can achieve better accuracy than single latency models while it enables us to support multiple latencies with a single model.
Investigating End-to-End ASR Architectures for Long Form Audio Transcription
This paper presents an overview and evaluation of some of the end-to-end ASR models on long-form audios.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition
Conformer-based models have become the dominant end-to-end architecture for speech processing tasks.
Ranked #1 on
Speech Recognition
on LibriSpeech test-other
Efficient Sequence Transduction by Jointly Predicting Tokens and Durations
TDT models for Speech Recognition achieve better accuracy and up to 2. 82X faster inference than conventional Transducers.
Intent Classification
Intent Classification and Slot Filling
+3
Multi-blank Transducers for Speech Recognition
This paper proposes a modification to RNN-Transducer (RNN-T) models for automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Damage Control During Domain Adaptation for Transducer Based Automatic Speech Recognition
Automatic speech recognition models are often adapted to improve their accuracy in a new domain.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
CTC Variations Through New WFST Topologies
This paper presents novel Weighted Finite-State Transducer (WFST) topologies to implement Connectionist Temporal Classification (CTC)-like algorithms for automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
CarneliNet: Neural Mixture Model for Automatic Speech Recognition
The basic idea is to introduce a parallel mixture of shallow networks instead of a very deep network.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
SPGISpeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognition
In the English speech-to-text (STT) machine learning task, acoustic models are conventionally trained on uncased Latin characters, and any necessary orthography (such as capitalization, punctuation, and denormalization of non-standard words) is imputed by separate post-processing models.
Ranked #3 on
Speech Recognition
on SPGISpeech
Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition
We propose Citrinet - a new end-to-end convolutional Connectionist Temporal Classification (CTC) based automatic speech recognition (ASR) model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Adversarial Attacks on Time Series
In this paper, we propose utilizing an adversarial transformation network (ATN) on a distilled model to attack various time series classification models.
Insights into LSTM Fully Convolutional Networks for Time Series Classification
In this paper, we perform a series of ablation tests (3627 experiments) on LSTM-FCN and ALSTM-FCN to provide a better understanding of the model and each of its sub-module.
Pathological Voice Classification Using Mel-Cepstrum Vectors and Support Vector Machine
Vocal disorders have affected several patients all over the world.
A Comprehensive Comparison between Neural Style Transfer and Universal Style Transfer
Style transfer aims to transfer arbitrary visual styles to content images.
Multivariate LSTM-FCNs for Time Series Classification
Over the past decade, multivariate time series classification has received great attention.
Ranked #1 on
Time Series Classification
on CharacterTrajectories
LSTM Fully Convolutional Networks for Time Series Classification
We propose the augmentation of fully convolutional networks with long short term memory recurrent neural network (LSTM RNN) sub-modules for time series classification.
Ranked #2 on
Outlier Detection
on ECG5000
