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Found 16 papers, 1 papers with code
UniEnc-CASSNAT: An Encoder-only Non-autoregressive ASR for Speech SSL Models
UniEnc-CASSNAT consists of only an encoder as the major module, which can be the SFM.
Towards Better Domain Adaptation for Self-supervised Models: A Case Study of Child ASR
With the proposed methods (E-APC and DRAFT), the relative WER improvements are even larger (30% and 19% on the OGI and MyST data, respectively) when compared to the models without using pretraining methods.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
A CTC Alignment-based Non-autoregressive Transformer for End-to-end Automatic Speech Recognition
During inference, an error-based alignment sampling method is investigated in depth to reduce the alignment mismatch in the training and testing processes.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Acoustic-aware Non-autoregressive Spell Correction with Mask Sample Decoding
As a result, we reduce the WER of a streaming TT from 7. 6% to 6. 5% on the Librispeech test-other data and the CER from 7. 3% to 6. 1% on the Aishell test data, respectively.
CTCBERT: Advancing Hidden-unit BERT with CTC Objectives
We examine CTCBERT on IDs from HuBERT Iter1, HuBERT Iter2, and PBERT.
DRAFT: A Novel Framework to Reduce Domain Shifting in Self-supervised Learning and Its Application to Children's ASR
However, models trained through SSL are biased to the pretraining data which is usually different from the data used in finetuning tasks, causing a domain shifting problem, and thus resulting in limited knowledge transfer.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Towards Better Meta-Initialization with Task Augmentation for Kindergarten-aged Speech Recognition
When data are scarce, the model might overfit to the training data, and hence good starting points for training are essential.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
LPC Augment: An LPC-Based ASR Data Augmentation Algorithm for Low and Zero-Resource Children's Dialects
This paper proposes a novel linear prediction coding-based data aug-mentation method for children's low and zero resource dialect ASR.
Low Resource German ASR with Untranscribed Data Spoken by Non-native Children -- INTERSPEECH 2021 Shared Task SPAPL System
~ 5 hours of transcribed data and ~ 60 hours of untranscribed data are provided to develop a German ASR system for children.
An Improved Single Step Non-autoregressive Transformer for Automatic Speech Recognition
For the analyses, we plot attention weight distributions in the decoders to visualize the relationships between token-level acoustic embeddings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Fundamental Frequency Feature Normalization and Data Augmentation for Child Speech Recognition
Because of the lack of publicly available young child speech data, feature extraction strategies such as feature normalization and data augmentation must be considered to successfully train child ASR systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Bi-APC: Bidirectional Autoregressive Predictive Coding for Unsupervised Pre-training and Its Application to Children's ASR
We present a bidirectional unsupervised model pre-training (UPT) method and apply it to children's automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
CASS-NAT: CTC Alignment-based Single Step Non-autoregressive Transformer for Speech Recognition
The information are used to extract acoustic representation for each token in parallel, referred to as token-level acoustic embedding which substitutes the word embedding in autoregressive transformer (AT) to achieve parallel generation in decoder.
Exploring the Use of an Unsupervised Autoregressive Model as a Shared Encoder for Text-Dependent Speaker Verification
A fusion of the x-vector/PLDA baseline and the SID/PLDA scores prior to PID fusion further improved performance by 15% indicating complementarity of the proposed approach to the x-vector system.
Improving Generalization of Transformer for Speech Recognition with Parallel Schedule Sampling and Relative Positional Embedding
Transformer has shown promising results in many sequence to sequence transformation tasks recently.
An Online Attention-based Model for Speech Recognition
In previous work, researchers have shown that such architectures can acquire comparable results to state-of-the-art ASR systems, especially when using a bidirectional encoder and global soft attention (GSA) mechanism.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
