Search Results for author:
Found 21 papers, 4 papers with code
Altogether: Image Captioning via Re-aligning Alt-text
This paper focuses on creating synthetic data to improve the quality of image captions.
Attention or Convolution: Transformer Encoders in Audio Language Models for Inference Efficiency
In this paper, we show that a simple self-supervised pre-trained audio model can achieve comparable inference efficiency to more complicated pre-trained models with speech transformer encoders.
FLAP: Fast Language-Audio Pre-training
We propose Fast Language-Audio Pre-training (FLAP), a self-supervised approach that efficiently and effectively learns aligned audio and language representations through masking, contrastive learning and reconstruction.
Efficient Speech Representation Learning with Low-Bit Quantization
With the development of hardware for machine learning, newer models often come at the cost of both increased sizes and computational complexity.
Continual Learning for On-Device Speech Recognition using Disentangled Conformers
Additionally, current speech recognition models and continual learning algorithms are not optimized to be compute-efficient.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
SUPERB @ SLT 2022: Challenge on Generalization and Efficiency of Self-Supervised Speech Representation Learning
We present the SUPERB challenge at SLT 2022, which aims at learning self-supervised speech representation for better performance, generalization, and efficiency.
TorchAudio: Building Blocks for Audio and Speech Processing
This document describes version 0. 10 of TorchAudio: building blocks for machine learning applications in the audio and speech processing domain.
Dynamic Encoder Transducer: A Flexible Solution For Trading Off Accuracy For Latency
DET gets similar accuracy as a baseline model with better latency on a large in-house data set by assigning a lightweight encoder for the beginning part of one utterance and a full-size encoder for the rest.
Semantic Distance: A New Metric for ASR Performance Analysis Towards Spoken Language Understanding
We define SemDist as the distance between a reference and hypothesis pair in a sentence-level embedding space.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+14
Benchmarking LF-MMI, CTC and RNN-T Criteria for Streaming ASR
In this work, to measure the accuracy and efficiency for a latency-controlled streaming automatic speech recognition (ASR) application, we perform comprehensive evaluations on three popular training criteria: LF-MMI, CTC and RNN-T.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Alignment Restricted Streaming Recurrent Neural Network Transducer
There is a growing interest in the speech community in developing Recurrent Neural Network Transducer (RNN-T) models for automatic speech recognition (ASR) applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Streaming Attention-Based Models with Augmented Memory for End-to-End Speech Recognition
Attention-based models have been gaining popularity recently for their strong performance demonstrated in fields such as machine translation and automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Transformer in action: a comparative study of transformer-based acoustic models for large scale speech recognition applications
We compare the transformer based acoustic models with their LSTM counterparts on industrial scale tasks.
Emformer: Efficient Memory Transformer Based Acoustic Model For Low Latency Streaming Speech Recognition
For a low latency scenario with an average latency of 80 ms, Emformer achieves WER $3. 01\%$ on test-clean and $7. 09\%$ on test-other.
Weak-Attention Suppression For Transformer Based Speech Recognition
Transformers, originally proposed for natural language processing (NLP) tasks, have recently achieved great success in automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Streaming Transformer-based Acoustic Models Using Self-attention with Augmented Memory
The memory bankstores the embedding information for all the processed seg-ments.
AIPNet: Generative Adversarial Pre-training of Accent-invariant Networks for End-to-end Speech Recognition
As one of the major sources in speech variability, accents have posed a grand challenge to the robustness of speech recognition systems.
RNN-T For Latency Controlled ASR With Improved Beam Search
Neural transducer-based systems such as RNN Transducers (RNN-T) for automatic speech recognition (ASR) blend the individual components of a traditional hybrid ASR systems (acoustic model, language model, punctuation model, inverse text normalization) into one single model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Transformer-Transducer: End-to-End Speech Recognition with Self-Attention
We explore options to use Transformer networks in neural transducer for end-to-end speech recognition.
Domain Adversarial Training for Accented Speech Recognition
In this paper, we propose a domain adversarial training (DAT) algorithm to alleviate the accented speech recognition problem.
Training Augmentation with Adversarial Examples for Robust Speech Recognition
This paper explores the use of adversarial examples in training speech recognition systems to increase robustness of deep neural network acoustic models.
