Results 151 to 160 of about 76,886 (181)

Connectionist temporal classification loss for vector quantized variational autoencoder in zero-shot voice conversion

Digit. Signal Process., 2021
Hao Huang
exaly   +2 more sources

CTC2: End-to-End Drum Transcription Based on Connectionist Temporal Classification With Constant Tempo Constraint

Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2023
This paper describes end-to-end automatic drum transcription for directly estimating a drum score from an audio signal of popular music using non-aligned paired data.
Daichi Kamakura, Eita Nakamura, Kazuyoshi Yoshii   +2 more
semanticscholar   +1 more source

Chinese Audio Transcription Using Connectionist Temporal Classification

Proceedings of the 8th International Conference on Computer and Communications Management, 2020
Mandarin is one of the global languages that have large users and speakers. There are several important factors for learners to be an expert in Mandarin. To be able to communicate properly, mastery in Chinese character (hanzi) and pīnyīn are required. We develop an Android-based app to help students who learn Mandarin.
Jansen, Dyah Erny Herwindiati, Janson Hendryli   +2 more
openaire   +1 more source

Query-by-Example on-Device Keyword Spotting using Convolutional Recurrent Neural Network and Connectionist Temporal Classification

International Conference on the Software Process, 2021
Keyword spotting (KWS) is an essential feature for speech-based applications on mobile devices. For the sake of reducing power consumption and improving robustness on substandard pronunciations of KWS systems, this paper proposes a query-by-example on ...
Meirong Li, Shaoying Zhang, Chuanxu Cheng, Xue Wen   +3 more
semanticscholar   +1 more source

Connectionist temporal classification

Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006
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.
Alex Graves, Santiago Fernández, Faustino J. Gomez, Jürgen Schmidhuber   +3 more
openaire   +1 more source

Improving Transformer-Based End-to-End Speech Recognition with Connectionist Temporal Classification and Language Model Integration

Interspeech, 2019
The state-of-the-art neural network architecture named Trans-former has been used successfully for many sequence-to-sequence transformation tasks. The advantage of this architecture is that it has a fast iteration speed in the training stage because ...
Shigeki Karita, Nelson Yalta, Shinji Watanabe, Marc Delcroix, A. Ogawa, T. Nakatani   +5 more
semanticscholar   +1 more source

Context-Aware Non-Autoregressive Document-Level Translation with Sentence-Aligned Connectionist Temporal Classification

International Conference on Language Resources and Evaluation
Previous studies employ the autoregressive translation (AT) paradigm in the document-to-document neural machine translation. These methods extend the translation unit from a single sentence to a pseudo-document and encodes the full pseudo-document ...
Hao Yu, Kaiyu Huang, Anqi Zhao, Junpeng Liu, Degen Huang   +4 more
semanticscholar   +1 more source

Deep Learning Model Efficient Lip Reading by Using Connectionist Temporal Classification Algorithm

Students Conference on Engineering and Systems
The technique of translating text from a person's mouth movements is said to be lipreading. The main work of lip reading is the process that is divided into two stages using old traditional techniques: prediction of a speaker's lips and producing or ...
B. Vidhya, R. O, K. Yazhini
semanticscholar   +1 more source

Modeling intra-label dynamics in connectionist temporal classification

2017 7th International Conference on Computer and Knowledge Engineering (ICCKE), 2017
Most sequence processing tasks can be cast as a problem of mapping a sequence of observations into a sequence of labels. This is a very difficult problem since the association between input data sequences and output label sequences is not given at the frame level. Recurrent neural networks (RNNs) equipped with connectionist temporal classification (CTC)
Ashkan Sadeghi Lotfabadi, Kamaledin Ghiasi-Shirazi, Ahad Harati   +2 more
openaire   +1 more source

Two-Stage Polyphonic Sound Event Detection Based on Faster R-CNN-LSTM with Multi-Token Connectionist Temporal Classification

Interspeech, 2020
We propose a two-stage sound event detection (SED) model to deal with sound events overlapping in time-frequency. In the first stage which consists of a faster R-CNN and an attention-LSTM, each log-mel spectrogram segment is divided into one or more ...
Inyoung Park, Hong Kook Kim
semanticscholar   +1 more source