Image classification using convolutional neural network (CNN) and recurrent neural network (RNN): A review
P Dhruv, S Naskar - … learning and information processing: proceedings of …, 2020 - Springer
With the advent of technologies, real-time data is essentially required for future
development. Everyday, a huge amount of visual data is being collected, but to use it …
development. Everyday, a huge amount of visual data is being collected, but to use it …
Word error rate estimation for speech recognition: e-WER
Measuring the performance of automatic speech recognition (ASR) systems requires
manually transcribed data in order to compute the word error rate (WER), which is often time …
manually transcribed data in order to compute the word error rate (WER), which is often time …
Error detection and accuracy estimation in automatic speech recognition using deep bidirectional recurrent neural networks
Recurrent neural networks (RNNs) have recently been applied as the classifiers for
sequential labeling problems. In this paper, deep bidirectional RNNs (DBRNNs) are applied …
sequential labeling problems. In this paper, deep bidirectional RNNs (DBRNNs) are applied …
Spoken SQuAD: A study of mitigating the impact of speech recognition errors on listening comprehension
Reading comprehension has been widely studied. One of the most representative reading
comprehension tasks is Stanford Question Answering Dataset (SQuAD), on which machine …
comprehension tasks is Stanford Question Answering Dataset (SQuAD), on which machine …
Estimating confidence scores on ASR results using recurrent neural networks
K Kalgaonkar, C Liu, Y Gong… - 2015 IEEE International …, 2015 - ieeexplore.ieee.org
In this paper we present a confidence estimation system using recurrent neural networks
(RNN) and compare it to a traditional multilayered perception (MLP) based system. The …
(RNN) and compare it to a traditional multilayered perception (MLP) based system. The …
Analyzing uncertainties in speech recognition using dropout
The performance of Automatic Speech Recognition (ASR) systems is often measured using
Word Error Rates (WER) which requires time-consuming and expensive manually …
Word Error Rates (WER) which requires time-consuming and expensive manually …
Using phoneme representations to build predictive models robust to ASR errors
Even though Automatic Speech Recognition (ASR) systems significantly improved over the
last decade, they still introduce a lot of errors when they transcribe voice to text. One of the …
last decade, they still introduce a lot of errors when they transcribe voice to text. One of the …
Learning from past mistakes: improving automatic speech recognition output via noisy-clean phrase context modeling
Automatic speech recognition (ASR) systems often make unrecoverable errors due to
subsystem pruning (acoustic, language and pronunciation models); for example, pruning …
subsystem pruning (acoustic, language and pronunciation models); for example, pruning …
ASR error detection and recognition rate estimation using deep bidirectional recurrent neural networks
Recurrent neural networks (RNNs) have recently been applied as the classifiers for
sequential labeling problems. In this paper, deep bidirectional RNNs (DBRNNs) are applied …
sequential labeling problems. In this paper, deep bidirectional RNNs (DBRNNs) are applied …
End-to-end speech to intent prediction to improve E-commerce customer support voicebot in Hindi and English
A Goyal, A Singh, N Garera - arXiv preprint arXiv:2211.07710, 2022 - arxiv.org
Automation of on-call customer support relies heavily on accurate and efficient speech-to-
intent (S2I) systems. Building such systems using multi-component pipelines can pose …
intent (S2I) systems. Building such systems using multi-component pipelines can pose …