2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDSPE-27.5
Paper Title NEURAL UTTERANCE CONFIDENCE MEASURE FOR RNN-TRANSDUCERS AND TWO PASS MODELS
Authors Ashutosh Gupta, Ankur Kumar, Samsung Research Institute, Bangelore, India; Dhananjaya Gowda, Kwangyoun Kim, Samsung Research Korea, South Korea; Sachin Singh, Samsung Bangalore, India; Shatrughan Singh, Samsung Research, India; Chanwoo Kim, Samsung Korea, South Korea
SessionSPE-27: Speech Recognition 9: Confidence Measures
LocationGather.Town
Session Time:Wednesday, 09 June, 16:30 - 17:15
Presentation Time:Wednesday, 09 June, 16:30 - 17:15
Presentation Poster
Topic Speech Processing: [SPE-GASR] General Topics in Speech Recognition
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Virtual Presentation  Click here to watch in the Virtual Conference
Abstract In this paper, we propose methods to compute confidence score on the predictions made by an end-to-end speech recognition model in a 2-pass framework. We use RNN-Transducer for a streaming model, and an attention-based decoder for the second pass model. We use neural technique to compute the confidence score, and experiment with various combinations of features from RNN-Transducer and second pass models.The neural confidence score model is trained as a binary classification task to accept or reject a prediction made by speech recognition model. The model is evaluated in a distributed speech recognition environment, and performs significantly better when features from second pass model are used as com-pared to the features from streaming model