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Found 11 papers, 1 papers with code
Towards End-to-End Integration of Dialog History for Improved Spoken Language Understanding
In the absence of gold transcripts to fine-tune an ASR model, our model outperforms this baseline by a significant margin of 10% absolute F1 score.
Tokenwise Contrastive Pretraining for Finer Speech-to-BERT Alignment in End-to-End Speech-to-Intent Systems
Recent advances in End-to-End (E2E) Spoken Language Understanding (SLU) have been primarily due to effective pretraining of speech representations.
Towards Reducing the Need for Speech Training Data To Build Spoken Language Understanding Systems
In this paper, we propose a novel text representation and training methodology that allows E2E SLU systems to be effectively constructed using these text resources.
Integrating Text Inputs For Training and Adapting RNN Transducer ASR Models
We observe 20-45% relative word error rate (WER) reduction in these settings with this novel LM style customization technique using only unpaired text data from the new domains.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Improving End-to-End Models for Set Prediction in Spoken Language Understanding
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.
Integrating Dialog History into End-to-End Spoken Language Understanding Systems
End-to-end spoken language understanding (SLU) systems that process human-human or human-computer interactions are often context independent and process each turn of a conversation independently.
RNN Transducer Models For Spoken Language Understanding
We present a comprehensive study on building and adapting RNN transducer (RNN-T) models for spoken language understanding(SLU).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
End-to-end spoken language understanding using transformer networks and self-supervised pre-trained features
Transformer networks and self-supervised pre-training have consistently delivered state-of-art results in the field of natural language processing (NLP); however, their merits in the field of spoken language understanding (SLU) still need further investigation.
End-to-End Spoken Language Understanding Without Full Transcripts
For our speech-to-entities experiments on the ATIS corpus, both the CTC and attention models showed impressive ability to skip non-entity words: there was little degradation when trained on just entities versus full transcripts.
The IBM 2016 English Conversational Telephone Speech Recognition System
We describe a collection of acoustic and language modeling techniques that lowered the word error rate of our English conversational telephone LVCSR system to a record 6. 6% on the Switchboard subset of the Hub5 2000 evaluation testset.
Ranked #5 on
Speech Recognition
on swb_hub_500 WER fullSWBCH
The IBM 2015 English Conversational Telephone Speech Recognition System
We describe the latest improvements to the IBM English conversational telephone speech recognition system.
Ranked #11 on
Speech Recognition
on Switchboard + Hub500
