Search Results for author:
Found 16 papers, 6 papers with code
Improving ASR Contextual Biasing with Guided Attention
Compared to studies with similar motivations, the proposed loss operates directly on the cross attention weights and is easier to implement.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Generative Context-aware Fine-tuning of Self-supervised Speech Models
Considering the recent advances in generative large language models (LLM), we hypothesize that an LLM could generate useful context information using the preceding text.
A Comparative Study on E-Branchformer vs Conformer in Speech Recognition, Translation, and Understanding Tasks
Conformer, a convolution-augmented Transformer variant, has become the de facto encoder architecture for speech processing due to its superior performance in various tasks, including automatic speech recognition (ASR), speech translation (ST) and spoken language understanding (SLU).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Structured Pruning of Self-Supervised Pre-trained Models for Speech Recognition and Understanding
Self-supervised speech representation learning (SSL) has shown to be effective in various downstream tasks, but SSL models are usually large and slow.
Context-aware Fine-tuning of Self-supervised Speech Models
During the fine-tuning stage, we introduce an auxiliary loss that encourages this context embedding vector to be similar to context vectors of surrounding segments.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
E-Branchformer: Branchformer with Enhanced merging for speech recognition
Conformer, combining convolution and self-attention sequentially to capture both local and global information, has shown remarkable performance and is currently regarded as the state-of-the-art for automatic speech recognition (ASR).
Ranked #9 on
Speech Recognition
on LibriSpeech test-other
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages
We introduce Wav2Seq, the first self-supervised approach to pre-train both parts of encoder-decoder models for speech data.
Ranked #3 on
Named Entity Recognition (NER)
on SLUE
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
SRU++: Pioneering Fast Recurrence with Attention for Speech Recognition
The Transformer architecture has been well adopted as a dominant architecture in most sequence transduction tasks including automatic speech recognition (ASR), since its attention mechanism excels in capturing long-range dependencies.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition
This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multi-mode Transformer Transducer with Stochastic Future Context
A Multi-mode ASR model can fulfill various latency requirements during inference -- when a larger latency becomes acceptable, the model can process longer future context to achieve higher accuracy and when a latency budget is not flexible, the model can be less dependent on future context but still achieve reliable accuracy.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Sequential Routing Framework: Fully Capsule Network-based Speech Recognition
Input sequences are capsulized then sliced by a window size.
Small energy masking for improved neural network training for end-to-end speech recognition
More specifically, a time-frequency bin is masked if the filterbank energy in this bin is less than a certain energy threshold.
Attention based on-device streaming speech recognition with large speech corpus
In this paper, we present a new on-device automatic speech recognition (ASR) system based on monotonic chunk-wise attention (MoChA) models trained with large (> 10K hours) corpus.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Improved Multi-Stage Training of Online Attention-based Encoder-Decoder Models
In this paper, we propose a refined multi-stage multi-task training strategy to improve the performance of online attention-based encoder-decoder (AED) models.
power-law nonlinearity with maximally uniform distribution criterion for improved neural network training in automatic speech recognition
With the power function-based MUD, we apply a power-function based nonlinearity where power function coefficients are chosen to maximize the likelihood assuming that nonlinearity outputs follow the uniform distribution.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
end-to-end training of a large vocabulary end-to-end speech recognition system
Our end-to-end speech recognition system built using this training infrastructure showed a 2. 44 % WER on test-clean of the LibriSpeech test set after applying shallow fusion with a Transformer language model (LM).
