Search Results for author:
Found 28 papers, 5 papers with code
From Captions to Visual Concepts and Back
The language model learns from a set of over 400, 000 image descriptions to capture the statistics of word usage.
Ranked #1 on
Image Captioning
on COCO Captions test
Language Models for Image Captioning: The Quirks and What Works
Two recent approaches have achieved state-of-the-art results in image captioning.
Sequence-to-Sequence Neural Net Models for Grapheme-to-Phoneme Conversion
We find that the simple side-conditioned generation approach is able to rival the state-of-the-art, and we are able to significantly advance the stat-of-the-art with bi-directional long short-term memory (LSTM) neural networks that use the same alignment information that is used in conventional approaches.
Attention with Intention for a Neural Network Conversation Model
The intention network is a recurrent network that models the dynamics of the intention process.
End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning
This paper presents a model for end-to-end learning of task-oriented dialog systems.
An Attentional Neural Conversation Model with Improved Specificity
Experimental results indicate that the model outperforms previously proposed neural conversation architectures, and that using specificity in the objective function significantly improves performances for both generation and retrieval.
Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning
End-to-end learning of recurrent neural networks (RNNs) is an attractive solution for dialog systems; however, current techniques are data-intensive and require thousands of dialogs to learn simple behaviors.
May I take your order? A Neural Model for Extracting Structured Information from Conversations
This is motivated by an actual system under development to assist in the order taking process.
Multilingual Graphemic Hybrid ASR with Massive Data Augmentation
Towards developing high-performing ASR for low-resource languages, approaches to address the lack of resources are to make use of data from multiple languages, and to augment the training data by creating acoustic variations.
From Senones to Chenones: Tied Context-Dependent Graphemes for Hybrid Speech Recognition
There is an implicit assumption that traditional hybrid approaches for automatic speech recognition (ASR) cannot directly model graphemes and need to rely on phonetic lexicons to get competitive performance, especially on English which has poor grapheme-phoneme correspondence.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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.
On Compositions of Transformations in Contrastive Self-Supervised Learning
In the image domain, excellent representations can be learned by inducing invariance to content-preserving transformations via noise contrastive learning.
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
Large scale weakly and semi-supervised learning for low-resource video ASR
Many semi- and weakly-supervised approaches have been investigated for overcoming the labeling cost of building high quality speech recognition systems.
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)
Contextual RNN-T For Open Domain ASR
By using an attention model and a biasing model to leverage the contextual metadata that accompanies a video, we observe a relative improvement of about 16% in Word Error Rate on Named Entities (WER-NE) for videos with related metadata.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multi-modal Self-Supervision from Generalized Data Transformations
In this paper, we show that, for videos, the answer is more complex, and that better results can be obtained by accounting for the interplay between invariance, distinctiveness, multiple modalities and time.
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
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
Kaizen: Continuously improving teacher using Exponential Moving Average for semi-supervised speech recognition
In this paper, we introduce the Kaizen framework that uses a continuously improving teacher to generate pseudo-labels for semi-supervised speech recognition (ASR).
