Search Results for author:
Found 39 papers, 6 papers with code
End-to-End Speech Recognition: A Survey
In the last decade of automatic speech recognition (ASR) research, the introduction of deep learning brought considerable reductions in word error rate of more than 50% relative, compared to modeling without deep learning.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Variable Attention Masking for Configurable Transformer Transducer Speech Recognition
This work studies the use of attention masking in transformer transducer based speech recognition for building a single configurable model for different deployment scenarios.
Extended Graph Temporal Classification for Multi-Speaker End-to-End ASR
As an example application, we use the extended GTC (GTC-e) for the multi-speaker speech recognition task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Sequence Transduction with Graph-based Supervision
The recurrent neural network transducer (RNN-T) objective plays a major role in building today's best automatic speech recognition (ASR) systems for production.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Audio-Visual Scene-Aware Dialog and Reasoning using Audio-Visual Transformers with Joint Student-Teacher Learning
In previous work, we have proposed the Audio-Visual Scene-Aware Dialog (AVSD) task, collected an AVSD dataset, developed AVSD technologies, and hosted an AVSD challenge track at both the 7th and 8th Dialog System Technology Challenges (DSTC7, DSTC8).
Advancing Momentum Pseudo-Labeling with Conformer and Initialization Strategy
Pseudo-labeling (PL), a semi-supervised learning (SSL) method where a seed model performs self-training using pseudo-labels generated from untranscribed speech, has been shown to enhance the performance of end-to-end automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Optimizing Latency for Online Video CaptioningUsing Audio-Visual Transformers
A CNN-based timing detector is also trained to detect a proper output timing, where the captions generated by the two Trans-formers become sufficiently close to each other.
Dual Causal/Non-Causal Self-Attention for Streaming End-to-End Speech Recognition
Attention-based end-to-end automatic speech recognition (ASR) systems have recently demonstrated state-of-the-art results for numerous tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Momentum Pseudo-Labeling for Semi-Supervised Speech Recognition
MPL consists of a pair of online and offline models that interact and learn from each other, inspired by the mean teacher method.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Advanced Long-context End-to-end Speech Recognition Using Context-expanded Transformers
In this paper, we extend our prior work by (1) introducing the Conformer architecture to further improve the accuracy, (2) accelerating the decoding process with a novel activation recycling technique, and (3) enabling streaming decoding with triggered attention.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Capturing Multi-Resolution Context by Dilated Self-Attention
The restricted self-attention allows attention to neighboring frames of the query at a high resolution, and the dilation mechanism summarizes distant information to allow attending to it with a lower resolution.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Unsupervised Domain Adaptation for Speech Recognition via Uncertainty Driven Self-Training
The performance of automatic speech recognition (ASR) systems typically degrades significantly when the training and test data domains are mismatched.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Semi-Supervised Speech Recognition via Graph-based Temporal Classification
However, alternative ASR hypotheses of an N-best list can provide more accurate labels for an unlabeled speech utterance and also reflect uncertainties of the seed ASR model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Multi-Pass Transformer for Machine Translation
In contrast with previous approaches where information flows only towards deeper layers of a stack, we consider a multi-pass transformer (MPT) architecture in which earlier layers are allowed to process information in light of the output of later layers.
Unsupervised Speaker Adaptation using Attention-based Speaker Memory for End-to-End ASR
We propose an unsupervised speaker adaptation method inspired by the neural Turing machine for end-to-end (E2E) automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Streaming automatic speech recognition with the transformer model
Encoder-decoder based sequence-to-sequence models have demonstrated state-of-the-art results in end-to-end automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Comparative Study on Transformer vs RNN in Speech Applications
Sequence-to-sequence models have been widely used in end-to-end speech processing, for example, automatic speech recognition (ASR), speech translation (ST), and text-to-speech (TTS).
Ranked #12 on
Speech Recognition
on AISHELL-1
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Multi-Stream End-to-End Speech Recognition
Two representative framework have been proposed and discussed, which are Multi-Encoder Multi-Resolution (MEM-Res) framework and Multi-Encoder Multi-Array (MEM-Array) framework, respectively.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text
Such techniques derive training procedures and losses able to leverage unpaired speech and/or text data by combining ASR with Text-to-Speech (TTS) models.
Ranked #33 on
Semi-Supervised Image Classification
on ImageNet - 10% labeled data
(Top 5 Accuracy metric)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multi-encoder multi-resolution framework for end-to-end speech recognition
In this work, we present a novel Multi-Encoder Multi-Resolution (MEMR) framework based on the joint CTC/Attention model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Vectorization of hypotheses and speech for faster beam search in encoder decoder-based speech recognition
In this paper, we propose a parallelism technique for beam search, which accelerates the search process by vectorizing multiple hypotheses to eliminate the for-loop program.
Stream attention-based multi-array end-to-end speech recognition
Automatic Speech Recognition (ASR) using multiple microphone arrays has achieved great success in the far-field robustness.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Promising Accurate Prefix Boosting for sequence-to-sequence ASR
In this paper, we present promising accurate prefix boosting (PAPB), a discriminative training technique for attention based sequence-to-sequence (seq2seq) ASR.
Analysis of Multilingual Sequence-to-Sequence speech recognition systems
This paper investigates the applications of various multilingual approaches developed in conventional hidden Markov model (HMM) systems to sequence-to-sequence (seq2seq) automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
CNN-based MultiChannel End-to-End Speech Recognition for everyday home environments
By employing a convolutional neural network (CNN)-based multichannel end-to-end speech recognition system, this study attempts to overcome the presents difficulties in everyday environments.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Cycle-consistency training for end-to-end speech recognition
To solve this problem, this work presents a loss that is based on the speech encoder state sequence instead of the raw speech signal.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modeling
In this work, we attempt to use data from 10 BABEL languages to build a multi-lingual seq2seq model as a prior model, and then port them towards 4 other BABEL languages using transfer learning approach.
Language Modelling
Sequence-To-Sequence Speech Recognition
+2
End-to-End Multi-Lingual Multi-Speaker Speech Recognition
Several multi-lingual ASR systems were recently proposed based on a monolithic neural network architecture without language-dependent modules, showing that modeling of multiple languages is well within the capabilities of an end-to-end framework.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
End-to-end Speech Recognition with Word-based RNN Language Models
This paper investigates the impact of word-based RNN language models (RNN-LMs) on the performance of end-to-end automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Back-Translation-Style Data Augmentation for End-to-End ASR
In this paper we propose a novel data augmentation method for attention-based end-to-end automatic speech recognition (E2E-ASR), utilizing a large amount of text which is not paired with speech signals.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
End-to-End Audio Visual Scene-Aware Dialog using Multimodal Attention-Based Video Features
We introduce a new dataset of dialogs about videos of human behaviors.
A Purely End-to-end System for Multi-speaker Speech Recognition
In this paper, we propose a new sequence-to-sequence framework to directly decode multiple label sequences from a single speech sequence by unifying source separation and speech recognition functions in an end-to-end manner.
ESPnet: End-to-End Speech Processing Toolkit
This paper introduces a new open source platform for end-to-end speech processing named ESPnet.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Joint CTC/attention decoding for end-to-end speech recognition
End-to-end automatic speech recognition (ASR) has become a popular alternative to conventional DNN/HMM systems because it avoids the need for linguistic resources such as pronunciation dictionary, tokenization, and context-dependency trees, leading to a greatly simplified model-building process.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
End-to-end Conversation Modeling Track in DSTC6
For example, Ghazvininejad et al. proposed a knowledge grounded neural conversation model [3], where the research is aiming at combining conversational dialogs with task-oriented knowledge using unstructured data such as Twitter data for conversation and Foursquare data for external knowledge. However, the task is still limited to a restaurant information service, and has not yet been tested with a wide variety of dialog tasks.
Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM
The CTC network sits on top of the encoder and is jointly trained with the attention-based decoder.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multichannel End-to-end Speech Recognition
The field of speech recognition is in the midst of a paradigm shift: end-to-end neural networks are challenging the dominance of hidden Markov models as a core technology.
Attention-Based Multimodal Fusion for Video Description
Currently successful methods for video description are based on encoder-decoder sentence generation using recur-rent neural networks (RNNs).
Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning
Recently, there has been an increasing interest in end-to-end speech recognition that directly transcribes speech to text without any predefined alignments.
