End-to-end speech recognition models using limited training data*
نویسندگان
چکیده
منابع مشابه
End-to-End Speech Recognition Models
For the past few decades, the bane of Automatic Speech Recognition (ASR) systems have been phonemes and Hidden Markov Models (HMMs). HMMs assume conditional independence between observations, and the reliance on explicit phonetic representations requires expensive handcrafted pronunciation dictionaries. Learning is often via detached proxy problems, and there especially exists a disconnect betw...
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Achieving high accuracy with end-to-end speech recognizers requires careful parameter initialization prior to training. Otherwise, the networks may fail to find a good local optimum. This is particularly true for low-latency online networks, such as unidirectional LSTMs. Currently, the best strategy to train such systems is to bootstrap the training from a tied-triphone system. However, this is...
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Several end-to-end deep learning approaches have been recently presented which extract either audio or visual features from the input images or audio signals and perform speech recognition. However, research on end-to-end audiovisual models is very limited. In this work, we present an end-toend audiovisual model based on residual networks and Bidirectional Gated Recurrent Units (BGRUs). To the ...
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The field of speech recognition is in the midst of a paradigm shift: end-to-end neural networks are challenging the dominance of hidden Markov models as a core technology. Using an attention mechanism in a recurrent encoder-decoder architecture solves the dynamic time alignment problem, allowing joint end-to-end training of the acoustic and language modeling components. In this paper we extend ...
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ژورنال
عنوان ژورنال: Phonetics and Speech Sciences
سال: 2020
ISSN: 2005-8063,2586-5854
DOI: 10.13064/ksss.2020.12.4.063