{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/end-to-end-speech-recognition-using-a-high","title":"End-To-End Speech Recognition Using A High Rank LSTM-CTC Based Model","arxiv_id":"1903.05261","date":"2019-03-12","proceeding":null,"authors":["Yangyang Shi","Mei-Yuh Hwang","Xin Lei"],"abstract":"Long Short Term Memory Connectionist Temporal Classification (LSTM-CTC) based\nend-to-end models are widely used in speech recognition due to its simplicity\nin training and efficiency in decoding. In conventional LSTM-CTC based models,\na bottleneck projection matrix maps the hidden feature vectors obtained from\nLSTM to softmax output layer. In this paper, we propose to use a high rank\nprojection layer to replace the projection matrix. The output from the high\nrank projection layer is a weighted combination of vectors that are projected\nfrom the hidden feature vectors via different projection matrices and\nnon-linear activation function. The high rank projection layer is able to\nimprove the expressiveness of LSTM-CTC models. The experimental results show\nthat on Wall Street Journal (WSJ) corpus and LibriSpeech data set, the proposed\nmethod achieves 4%-6% relative word error rate (WER) reduction over the\nbaseline CTC system. They outperform other published CTC based end-to-end (E2E)\nmodels under the condition that no external data or data augmentation is\napplied. Code has been made available at https://github.com/mobvoi/lstm_ctc.","url_abs":"http://arxiv.org/abs/1903.05261v1","url_pdf":"http://arxiv.org/pdf/1903.05261v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"end-to-end-speech-recognition-using-a-high","repo_url":"https://github.com/mobvoi/lstm_ctc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"speech-recognition","task_name":"Speech Recognition"},{"task_slug":"speech-recognition-1","task_name":"speech-recognition"}],"methods":[{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}