{"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/asapp-asr-multistream-cnn-and-self-attentive","title":"ASAPP-ASR: Multistream CNN and Self-Attentive SRU for SOTA Speech Recognition","arxiv_id":"2005.10469","date":"2020-05-21","proceeding":null,"authors":["Jing Pan","Joshua Shapiro","Jeremy Wohlwend","Kyu J. Han","Tao Lei","Tao Ma"],"abstract":"In this paper we present state-of-the-art (SOTA) performance on the LibriSpeech corpus with two novel neural network architectures, a multistream CNN for acoustic modeling and a self-attentive simple recurrent unit (SRU) for language modeling. In the hybrid ASR framework, the multistream CNN acoustic model processes an input of speech frames in multiple parallel pipelines where each stream has a unique dilation rate for diversity. Trained with the SpecAugment data augmentation method, it achieves relative word error rate (WER) improvements of 4% on test-clean and 14% on test-other. We further improve the performance via N-best rescoring using a 24-layer self-attentive SRU language model, achieving WERs of 1.75% on test-clean and 4.46% on test-other.","url_abs":"https://arxiv.org/abs/2005.10469v1","url_pdf":"https://arxiv.org/pdf/2005.10469v1.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":[],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"speech-recognition","task_name":"Speech Recognition"},{"task_slug":"speech-recognition-1","task_name":"speech-recognition"}],"methods":[{"method_slug":"highway-layer","method_name":"Highway Layer"},{"method_slug":"sru","method_name":"SRU"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/speech-recognition-on-librispeech-test-clean","task":"Speech Recognition","dataset":"LibriSpeech test-clean","model":"Multistream CNN with Self-Attentive SRU (WER includes text normalization)","rank_in_archive_order":10,"of":64,"metrics":{"Word Error Rate (WER)":"1.75"},"uses_additional_data":true},{"leaderboard":"/sota/speech-recognition-on-librispeech-test-other","task":"Speech Recognition","dataset":"LibriSpeech test-other","model":"Multistream CNN with Self-Attentive SRU","rank_in_archive_order":26,"of":53,"metrics":{"Word Error Rate (WER)":"4.46"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2005.10469","atlas_url":"https://app.syntology.ai/?focus=2005.10469","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}