Proceedings ArticleDOI
Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
Alex Graves,Santiago Fernández,Faustino Gomez,Jürgen Schmidhuber +3 more
- pp 369-376
TLDR
This paper presents a novel method for training RNNs to label unsegmented sequences directly, thereby solving both problems of sequence learning and post-processing.Abstract:
Many real-world sequence learning tasks require the prediction of sequences of labels from noisy, unsegmented input data. In speech recognition, for example, an acoustic signal is transcribed into words or sub-word units. Recurrent neural networks (RNNs) are powerful sequence learners that would seem well suited to such tasks. However, because they require pre-segmented training data, and post-processing to transform their outputs into label sequences, their applicability has so far been limited. This paper presents a novel method for training RNNs to label unsegmented sequences directly, thereby solving both problems. An experiment on the TIMIT speech corpus demonstrates its advantages over both a baseline HMM and a hybrid HMM-RNN.read more
Citations
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Proceedings ArticleDOI
End-to-End Speech Emotion Recognition Combined with Acoustic-to-Word ASR Model.
TL;DR: This paper proposes speech emotion recognition (SER) combined with an acoustic-to-word automatic speech recognition (ASR) model, which has achieved a 68.63% weighted accuracy and 69.67% unweighted accuracy on the IEMOCAP database, which is state-of-the-art performance.
Proceedings ArticleDOI
Exploiting Depth and Highway Connections in Convolutional Recurrent Deep Neural Networks for Speech Recognition.
TL;DR: The CLDNN model is extended by introducing a highway connection between LSTM layers, which enables direct information flow from cells of lower layers to cells of upper layers, and is able to better exploit the advantages of a deeper structure.
Proceedings Article
Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search
TL;DR: Glow-TTS is proposed, a flow-based generative model for parallel TTS that does not require any external aligner and obtains an order-of-magnitude speed-up over the autoregressive model, Tacotron 2, at synthesis with comparable speech quality.
Proceedings ArticleDOI
Domain Adaptation via Teacher-Student Learning for End-to-End Speech Recognition
TL;DR: This work extends the T/S learning to large-scale unsupervised domain adaptation of an attention-based end-to-end (E2E) model through two levels of knowledge transfer: teacher's token posteriors as soft labels and one-best predictions as decoder guidance.
Posted Content
Attention based on-device streaming speech recognition with large speech corpus
Kwangyoun Kim,Kyungmin Lee,Dhananjaya Gowda,Junmo Park,Sung-Soo Kim,Sichen Jin,Young-Yoon Lee,Jinsu Yeo,Dae Hyun Kim,Seok-Yeong Jung,Jungin Lee,Myoungji Han,Chanwoo Kim +12 more
TL;DR: This paper presents a new on-device automatic speech recognition (ASR) system based on monotonic chunk-wise attention (MoChA) models trained with large (> 10K hours) corpus that attained around 90% of a word recognition rate for general domain mainly by using joint training of connectionist temporal classifier and cross entropy losses.
References
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