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Found 11 papers, 0 papers with code
FBK’s Neural Machine Translation Systems for IWSLT 2016
They leverage linguistic information such as lemmas and part-of-speech tags of the source words in the form of additional factors along with the words.
1SPU: 1-step Speech Processing Unit
Recent studies have made some progress in refining end-to-end (E2E) speech recognition encoders by applying Connectionist Temporal Classification (CTC) loss to enhance named entity recognition within transcriptions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Trustera: A Live Conversation Redaction System
Trustera, the first functional system that redacts personally identifiable information (PII) in real-time spoken conversations to remove agents' need to hear sensitive information while preserving the naturalness of live customer-agent conversations.
Automatic Speech Recognition
Natural Language Understanding
+2
Seq-2-Seq based Refinement of ASR Output for Spoken Name Capture
Person name capture from human speech is a difficult task in human-machine conversations.
Unsupervised Spoken Utterance Classification
An intelligent virtual assistant (IVA) enables effortless conversations in call routing through spoken utterance classification (SUC) which is a special form of spoken language understanding (SLU).
A Hybrid Approach to Scalable and Robust Spoken Language Understanding in Enterprise Virtual Agents
Spoken language understanding (SLU) extracts the intended mean- ing from a user utterance and is a critical component of conversational virtual agents.
Automatic Quality Estimation for ASR System Combination
In this paper we propose a novel variant of ROVER that takes advantage of ASR quality estimation (QE) for ranking the transcriptions at "segment level" instead of: i) relying on confidence scores, or ii) feeding ROVER with randomly ordered hypotheses.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
DNN adaptation by automatic quality estimation of ASR hypotheses
Our hypothesis is that significant improvements can be achieved by: i)automatically transcribing the evaluation data we are currently trying to recognise, and ii) selecting from it a subset of "good quality" instances based on the word error rate (WER) scores predicted by a QE component.
