2018
INTERSPEECH
INTERSPEECH 2018
Tone Recognition Using Lifters and CTC
Abstract
In this paper, we present a new method for recognizing tones in continuous speech for tonal languages. The method works by converting the speech signal to a cepstrogram, extracting a sequence of cepstral features using a convolutional neural network and predicting the underlying sequence of tones using a connectionist temporal classification (CTC) network. The performance of the proposed method is evaluated on a freely available Mandarin Chinese speech corpus, AISHELL-1 and is shown to outperform the existing techniques in the literature in terms of tone error rate (TER).
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Interdisciplinary Bridge
— Deep Learning and Machine Learning
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Keyword Pioneer
— tone error rate
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio