Search Results for author:
Found 14 papers, 3 papers with code
WavLLM: Towards Robust and Adaptive Speech Large Language Model
In this work, we introduce WavLLM, a robust and adaptive speech large language model with dual encoders, and a prompt-aware LoRA weight adapter, optimized by a two-stage curriculum learning approach.
COSMIC: Data Efficient Instruction-tuning For Speech In-Context Learning
With fewer than 20M trainable parameters and as little as 450 hours of English speech data for SQA generation, COSMIC exhibits emergent instruction-following and in-context learning capabilities in speech-to-text tasks.
Improving Stability in Simultaneous Speech Translation: A Revision-Controllable Decoding Approach
Simultaneous Speech-to-Text translation serves a critical role in real-time crosslingual communication.
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
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
Sensoring and Application of Multimodal Data for the Detection of Freezing of Gait in Parkinson's Disease
The accurate and reliable detection or prediction of freezing of gaits (FOG) is important for fall prevention in Parkinson's Disease (PD) and studying the physiological transitions during the occurrence of FOG.
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
Leveraging Pre-trained Language Model for Speech Sentiment Analysis
In this paper, we explore the use of pre-trained language models to learn sentiment information of written texts for speech sentiment analysis.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Multistream CNN for Robust Acoustic Modeling
When combined with self-attentive SRU LM rescoring, multistream CNN contributes for ASAPP to achieve the best WER of 1. 75% on test-clean in LibriSpeech.
ASAPP-ASR: Multistream CNN and Self-Attentive SRU for SOTA Speech Recognition
In this paper we present state-of-the-art (SOTA) performance on the LibriSpeech corpus with two novel neural network architectures, a multistream CNN for acoustic modeling and a self-attentive simple recurrent unit (SRU) for language modeling.
Ranked #7 on
Speech Recognition
on LibriSpeech test-clean
Benchmark Tests of Convolutional Neural Network and Graph Convolutional Network on HorovodRunner Enabled Spark Clusters
The freedom of fast iterations of distributed deep learning tasks is crucial for smaller companies to gain competitive advantages and market shares from big tech giants.
Adversarial Validation Approach to Concept Drift Problem in User Targeting Automation Systems at Uber
Here, we introduce an adversarial validation approach to concept drift problems in user targeting automation systems.
Order Matters at Fanatics Recommending Sequentially Ordered Products by LSTM Embedded with Word2Vec
A unique challenge for e-commerce recommendation is that customers are often interested in products that are more advanced than their already purchased products, but not reversed.
Moving Object Detection in Video Using Saliency Map and Subspace Learning
So there are undesirable false alarms and missed alarms in many algorithms of moving object detection.
