788 citations · 1.1k across the 33 of their papers we have counts for
14 papers · 1 filter
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…
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…
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…
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 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…
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…
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…
CNN-based MultiChannel End-to-End Speech Recognition for everyday home environments
Nelson Yalta, Shinji Watanabe, Takaaki Hori +2
Casual conversations involving multiple speakers and noises from surrounding devices are common in everyday environments, which degrades the performances of automatic speech recogn…
Language model integration based on memory control for sequence to sequence speech recognition
Jaejin Cho, Shinji Watanabe, Takaaki Hori +4
In this paper, we explore several new schemes to train a seq2seq model to integrate a pre-trained LM. Our proposed fusion methods focus on the memory cell state and the hidden stat…