Search Results for author:
Found 12 papers, 1 papers with code
O-1: Self-training with Oracle and 1-best Hypothesis
O-1 achieves 13\% to 25\% relative improvement over EMBR on the various datasets that SpeechStew comprises of, and a 12\% relative gap reduction with respect to the oracle WER over EMBR training on the in-house dataset.
Robust Knowledge Distillation from RNN-T Models With Noisy Training Labels Using Full-Sum Loss
Soft distillation is another popular KD method that distills the output logits of the teacher model.
Speaker adaptation for Wav2vec2 based dysarthric ASR
Speaker adaptation using fMLLR and xvectors have provided major gains for dysarthric speech with very little adaptation data.
Ask2Mask: Guided Data Selection for Masked Speech Modeling
They treat all unsupervised speech samples with equal weight, which hinders learning as not all samples have relevant information to learn meaningful representations.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
EAT: Enhanced ASR-TTS for Self-supervised Speech Recognition
Self-supervised ASR-TTS models suffer in out-of-domain data conditions.
BUT Opensat 2019 Speech Recognition System
The paper describes the BUT Automatic Speech Recognition (ASR) systems submitted for OpenSAT evaluations under two domain categories such as low resourced languages and public safety communications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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
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
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.
Transfer learning of language-independent end-to-end ASR with language model fusion
This work explores better adaptation methods to low-resource languages using an external language model (LM) under the framework of transfer learning.
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
Residual Memory Networks: Feed-forward approach to learn long temporal dependencies
In this paper we propose a residual memory neural network (RMN) architecture to model short-time dependencies using deep feed-forward layers having residual and time delayed connections.
