788 citations · 1.1k across the 33 of their papers we have counts for
39 papers
End-to-End Speech Recognition: A Survey
Rohit Prabhavalkar, Takaaki Hori, Tara N. Sainath +2
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, c…
Variable Attention Masking for Configurable Transformer Transducer Speech Recognition
Pawel Swietojanski, Stefan Braun, Dogan Can +9
This work studies the use of attention masking in transformer transducer based speech recognition for building a single configurable model for different deployment scenarios. We pr…
Extended Graph Temporal Classification for Multi-Speaker End-to-End ASR
Xuankai Chang, Niko Moritz, Takaaki Hori +2
Graph-based temporal classification (GTC), a generalized form of the connectionist temporal classification loss, was recently proposed to improve automatic speech recognition (ASR)…
Sequence Transduction with Graph-based Supervision
Niko Moritz, Takaaki Hori, Shinji Watanabe +1
The recurrent neural network transducer (RNN-T) objective plays a major role in building today's best automatic speech recognition (ASR) systems for production. Similarly to the co…
Audio-Visual Scene-Aware Dialog and Reasoning using Audio-Visual Transformers with Joint Student-Teacher Learning
Ankit P. Shah, Shijie Geng, Peng Gao +5
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 bo…
Advancing Momentum Pseudo-Labeling with Conformer and Initialization Strategy
Yosuke Higuchi, Niko Moritz, Jonathan Le Roux +1
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 e…
Optimizing Latency for Online Video CaptioningUsing Audio-Visual Transformers
Chiori Hori, Takaaki Hori, Jonathan Le Roux
Video captioning is an essential technology to understand scenes and describe events in natural language. To apply it to real-time monitoring, a system needs not only to describe e…
Dual Causal/Non-Causal Self-Attention for Streaming End-to-End Speech Recognition
Niko Moritz, Takaaki Hori, Jonathan Le Roux
Attention-based end-to-end automatic speech recognition (ASR) systems have recently demonstrated state-of-the-art results for numerous tasks. However, the application of self-atten…
Momentum Pseudo-Labeling for Semi-Supervised Speech Recognition
Yosuke Higuchi, Niko Moritz, Jonathan Le Roux +1
Pseudo-labeling (PL) has been shown to be effective in semi-supervised automatic speech recognition (ASR), where a base model is self-trained with pseudo-labels generated from unla…
Advanced Long-context End-to-end Speech Recognition Using Context-expanded Transformers
Takaaki Hori, Niko Moritz, Chiori Hori +1
This paper addresses end-to-end automatic speech recognition (ASR) for long audio recordings such as lecture and conversational speeches. Most end-to-end ASR models are designed to…
Capturing Multi-Resolution Context by Dilated Self-Attention
Niko Moritz, Takaaki Hori, Jonathan Le Roux
Self-attention has become an important and widely used neural network component that helped to establish new state-of-the-art results for various applications, such as machine tran…
The 2020 ESPnet update: new features, broadened applications, performance improvements, and future plans
Shinji Watanabe, Florian Boyer, Xuankai Chang +12
This paper describes the recent development of ESPnet (https://github.com/espnet/espnet), an end-to-end speech processing toolkit. This project was initiated in December 2017 to ma…
Unsupervised Domain Adaptation for Speech Recognition via Uncertainty Driven Self-Training
Sameer Khurana, Niko Moritz, Takaaki Hori +1
The performance of automatic speech recognition (ASR) systems typically degrades significantly when the training and test data domains are mismatched. In this paper, we show that s…
Semi-Supervised Speech Recognition via Graph-based Temporal Classification
Niko Moritz, Takaaki Hori, Jonathan Le Roux
Semi-supervised learning has demonstrated promising results in automatic speech recognition (ASR) by self-training using a seed ASR model with pseudo-labels generated for unlabeled…
Multi-Pass Transformer for Machine Translation
Peng Gao, Chiori Hori, Shijie Geng +2
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…
Unsupervised Speaker Adaptation using Attention-based Speaker Memory for End-to-End ASR
Leda Sarı, Niko Moritz, Takaaki Hori +1
We propose an unsupervised speaker adaptation method inspired by the neural Turing machine for end-to-end (E2E) automatic speech recognition (ASR). The proposed model contains a me…
Streaming automatic speech recognition with the transformer model
Niko Moritz, Takaaki Hori, Jonathan Le Roux
Encoder-decoder based sequence-to-sequence models have demonstrated state-of-the-art results in end-to-end automatic speech recognition (ASR). Recently, the transformer architectur…
A Comparative Study on Transformer vs RNN in Speech Applications
Shigeki Karita, Nanxin Chen, Tomoki Hayashi +10
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…
Multi-Stream End-to-End Speech Recognition
Ruizhi Li, Xiaofei Wang, Sri Harish Mallidi +3
Attention-based methods and Connectionist Temporal Classification (CTC) network have been promising research directions for end-to-end (E2E) Automatic Speech Recognition (ASR). The…
Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text
Murali Karthick Baskar, Shinji Watanabe, Ramon Astudillo +3
Sequence-to-sequence automatic speech recognition (ASR) models require large quantities of data to attain high performance. For this reason, there has been a recent surge in intere…
Multi-encoder multi-resolution framework for end-to-end speech recognition
Ruizhi Li, Xiaofei Wang, Sri Harish Mallidi +3
Attention-based methods and Connectionist Temporal Classification (CTC) network have been promising research directions for end-to-end Automatic Speech Recognition (ASR). The joint…
Vectorization of hypotheses and speech for faster beam search in encoder decoder-based speech recognition
Hiroshi Seki, Takaaki Hori, Shinji Watanabe
Attention-based encoder decoder network uses a left-to-right beam search algorithm in the inference step. The current beam search expands hypotheses and traverses the expanded hypo…
Analysis of Multilingual Sequence-to-Sequence speech recognition systems
Martin Karafiát, Murali Karthick Baskar, Shinji Watanabe +3
This paper investigates the applications of various multilingual approaches developed in conventional hidden Markov model (HMM) systems to sequence-to-sequence (seq2seq) automatic…
Promising Accurate Prefix Boosting for sequence-to-sequence ASR
Murali Karthick Baskar, Lukáš Burget, Shinji Watanabe +3
In this paper, we present promising accurate prefix boosting (PAPB), a discriminative training technique for attention based sequence-to-sequence (seq2seq) ASR. PAPB is devised to…