Search Results for author:
Found 16 papers, 3 papers with code
A Study on the Integration of Pre-trained SSL, ASR, LM and SLU Models for Spoken Language Understanding
Collecting sufficient labeled data for spoken language understanding (SLU) is expensive and time-consuming.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
BECTRA: Transducer-based End-to-End ASR with BERT-Enhanced Encoder
One crucial factor that makes this integration challenging lies in the vocabulary mismatch; the vocabulary constructed for a pre-trained LM is generally too large for E2E-ASR training and is likely to have a mismatch against a target ASR domain.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
InterMPL: Momentum Pseudo-Labeling with Intermediate CTC Loss
This paper presents InterMPL, a semi-supervised learning method of end-to-end automatic speech recognition (ASR) that performs pseudo-labeling (PL) with intermediate supervision.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
BERT Meets CTC: New Formulation of End-to-End Speech Recognition with Pre-trained Masked Language Model
This paper presents BERT-CTC, a novel formulation of end-to-end speech recognition that adapts BERT for connectionist temporal classification (CTC).
CTC Alignments Improve Autoregressive Translation
Connectionist Temporal Classification (CTC) is a widely used approach for automatic speech recognition (ASR) that performs conditionally independent monotonic alignment.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
ESPnet-ONNX: Bridging a Gap Between Research and Production
In the field of deep learning, researchers often focus on inventing novel neural network models and improving benchmarks.
Improving non-autoregressive end-to-end speech recognition with pre-trained acoustic and language models
The proposed NAR model significantly surpasses previous NAR systems on the AISHELL-1 benchmark and shows a potential for English tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
An Investigation of Enhancing CTC Model for Triggered Attention-based Streaming ASR
In the present paper, an attempt is made to combine Mask-CTC and the triggered attention mechanism to construct a streaming end-to-end automatic speech recognition (ASR) system that provides high performance with low latency.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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
A Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generation
Non-autoregressive (NAR) models simultaneously generate multiple outputs in a sequence, which significantly reduces the inference speed at the cost of accuracy drop compared to autoregressive baselines.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Hierarchical Conditional End-to-End ASR with CTC and Multi-Granular Subword Units
In this work, to promote the word-level representation learning in end-to-end ASR, we propose a hierarchical conditional model that is based on connectionist temporal classification (CTC).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Non-autoregressive End-to-end Speech Translation with Parallel Autoregressive Rescoring
We propose a unified NAR E2E-ST framework called Orthros, which has an NAR decoder and an auxiliary shallow AR decoder on top of the shared encoder.
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
Improved Mask-CTC for Non-Autoregressive End-to-End ASR
While Mask-CTC achieves remarkably fast inference speed, its recognition performance falls behind that of conventional autoregressive (AR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Orthros: Non-autoregressive End-to-end Speech Translation with Dual-decoder
Fast inference speed is an important goal towards real-world deployment of speech translation (ST) systems.
Mask CTC: Non-Autoregressive End-to-End ASR with CTC and Mask Predict
In this work, Mask CTC model is trained using a Transformer encoder-decoder with joint training of mask prediction and CTC.
Audio and Speech Processing Sound
