In particular, we show that the pronunciation FST can be built from a recurrent neural network (RNN) and tuned to provide rich yet constrained pronunciations.
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A general finite-state transducer (FST) framework is proposed to describe pronunciation learning algorithms and it is shown that the pronunciation FST can ...
Aug 24, 2017 · Abstract. Most speech recognition systems rely on pronunciation dictio- naries to provide accurate transcriptions. Typically, some pro-.
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How is RNN used in speech recognition?
The audio is specifically generated based on the noted pronunciations from the data set instead of using standard resources to guarantee that the exact ...
Jul 29, 2022 · By introducing the CI, the RNN-T model can overcome the homophone problem while utilizing the pronunciation information for extracting modeling ...
Our method uses a novel phonetic recurrent-neural-network-transducer (RNN-T) model that predicts phonemes, the smallest units of speech, from the learner's ...
In this paper, we propose a set of autoregressive phonetic Recurrent Neural Network Transducer (RNN-T) MDD models that are capable of capturing temporal ...
This paper simulates an incremental learning setup on a real- world voice assistant application employing an RNN-T based. ASR system. Given such an RNN-T model ...
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Posted: Sep 3, 2022
Posted: Sep 3, 2022
In this paper, we propose a set of autoregressive phonetic Recurrent Neural Network Transducer (RNN-T) MDD mod- els that are capable of capturing temporal ...