93 citations · 314 across the 37 of their papers we have counts for
10 papers · 1 filter
C2KD: Cross-Lingual Cross-Modal Knowledge Distillation for Multilingual Text-Video Retrieval
Andrew Rouditchenko, Yung-Sung Chuang, Nina Shvetsova +7
Multilingual text-video retrieval methods have improved significantly in recent years, but the performance for other languages lags behind English. We propose a Cross-Lingual Cross…
VQ-T: RNN Transducers using Vector-Quantized Prediction Network States
Jiatong Shi, George Saon, David Haws +2
Beam search, which is the dominant ASR decoding algorithm for end-to-end models, generates tree-structured hypotheses. However, recent studies have shown that decoding with hypothe…
Accelerating Inference and Language Model Fusion of Recurrent Neural Network Transducers via End-to-End 4-bit Quantization
Andrea Fasoli, Chia-Yu Chen, Mauricio Serrano +5
We report on aggressive quantization strategies that greatly accelerate inference of Recurrent Neural Network Transducers (RNN-T). We use a 4 bit integer representation for both we…
Towards End-to-End Integration of Dialog History for Improved Spoken Language Understanding
Vishal Sunder, Samuel Thomas, Hong-Kwang J. Kuo +3
Dialog history plays an important role in spoken language understanding (SLU) performance in a dialog system. For end-to-end (E2E) SLU, previous work has used dialog history in tex…
Tokenwise Contrastive Pretraining for Finer Speech-to-BERT Alignment in End-to-End Speech-to-Intent Systems
Vishal Sunder, Eric Fosler-Lussier, Samuel Thomas +2
Recent advances in End-to-End (E2E) Spoken Language Understanding (SLU) have been primarily due to effective pretraining of speech representations. One such pretraining paradigm is…
Improving Generalization of Deep Neural Network Acoustic Models with Length Perturbation and N-best Based Label Smoothing
Xiaodong Cui, George Saon, Tohru Nagano +4
We introduce two techniques, length perturbation and n-best based label smoothing, to improve generalization of deep neural network (DNN) acoustic models for automatic speech recog…
Towards Reducing the Need for Speech Training Data To Build Spoken Language Understanding Systems
Samuel Thomas, Hong-Kwang J. Kuo, Brian Kingsbury +1
The lack of speech data annotated with labels required for spoken language understanding (SLU) is often a major hurdle in building end-to-end (E2E) systems that can directly proces…
Integrating Text Inputs For Training and Adapting RNN Transducer ASR Models
Samuel Thomas, Brian Kingsbury, George Saon +1
Compared to hybrid automatic speech recognition (ASR) systems that use a modular architecture in which each component can be independently adapted to a new domain, recent end-to-en…
A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets
Zvi Kons, Aharon Satt, Hong-Kwang Kuo +4
Intent classifiers are vital to the successful operation of virtual agent systems. This is especially so in voice activated systems where the data can be noisy with many ambiguous…
Improving End-to-End Models for Set Prediction in Spoken Language Understanding
Hong-Kwang J. Kuo, Zoltan Tuske, Samuel Thomas +2
The goal of spoken language understanding (SLU) systems is to determine the meaning of the input speech signal, unlike speech recognition which aims to produce verbatim transcripts…