{"id":11496,"date":"2023-12-13T11:19:39","date_gmt":"2023-12-13T11:19:39","guid":{"rendered":"https:\/\/whiteriversmediasolutions.com\/Sony\/fiducial-focus-augmentation-for-facial-landmark-detection-summarised-copy\/"},"modified":"2023-12-14T12:02:17","modified_gmt":"2023-12-14T12:02:17","slug":"decoding-efficient-infusion-of-self-supervised-representations-in-automatic-speech-recognition","status":"publish","type":"post","link":"https:\/\/whiteriversmediasolutions.com\/Sony\/decoding-efficient-infusion-of-self-supervised-representations-in-automatic-speech-recognition\/","title":{"rendered":"Decoding \u2018Efficient infusion of self-supervised representations in Automatic Speech Recognition\u2019"},"content":{"rendered":"\t\t
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BLOGS<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Decoding \u2018Efficient infusion of self-supervised representations in Automatic Speech Recognition\u2019<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tBy Darshan Prabhu at Sony Research India\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t13th<\/sup> December 2023\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Darshan Prabhu from the Content Analysis- Audio team summarises the research paper titled \u2018Efficient infusion of self-supervised representations in Automatic Speech Recognition\u2019<\/a> which he has co-authored with Sai Ganesh Mirishkar and Pankaj Wasnik from Sony Research India.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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This paper will be presented by the team at the Poster Session at the Neural Information Processing Systems (NeurIPS) 3rd Workshop on Efficient Natural Language and Speech Processing<\/a> in December 2023, hosted in New Orleans from 10th-16th December 2023.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Summary<\/strong><\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Self-supervised learned (SSL) models such as Wav2vec<\/a> and HuBERT<\/a> have demonstrated state-of-the-art results on speech-related tasks. Given the effectiveness of such models, it is advantageous to use them in conventional Automatic Speech Recognition (ASR) systems. While some approaches suggest incorporating these models as an encoder or a frontend, training such systems is extremely slow and requires a lot of computation cycles. If you have a limited training budget, an alternative approach is to use only the representations from these SSL models instead of directly using them as part of your neural network. Since the focus now is solely on the representations, they can be extracted beforehand as part of pre-processing (which can be easily done with parallel jobs). This one-time step eliminates the need for the SSL model during training, significantly reducing training time while sacrificing a minimal amount of efficiency. It is worth noting that this approach is not new and has already been explored in the NLP setting, where representations from models like BERT are employed in the context of neural machine translation ([2002.06823] Incorporating BERT into Neural Machine Translation<\/a>).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Figure 1<\/em><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\tIn this work, we propose an end-to-end ASR architecture that efficiently integrates self-supervised model representations into the speech encoder. We accomplish this with a fusion layer that ranges from a simple framewise addition to a more complex cross-attention mechanism. Our complete architecture is illustrated in Figure 1. \n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Figure 2<\/em><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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Figure 3<\/em><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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For the fusion layer, we have explored two straightforward approaches, as depicted in Figures 2 and 3. Figure 2 provides an overview of Framewise addition-based fusion, which capitalizes on the linear relationship between the lengths of the representations. It uses subsampling to ensure that both representations are of equal length before performing frame-level addition. On the other hand, Figure 3 demonstrates the utilization of cross-attention to merge the representations. This approach is not dependent on the lengths of the representations and can accommodate representations of any size. Further information regarding these approaches can be found in our paper<\/a>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Key Findings<\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Figure 4<\/em><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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Figure 5<\/em><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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We conducted experiments on Librispeech-100 and Tedlium2 datasets, with different choices for the SSL model. Overall, we found that our approach shows a significant WER reduction, despite only a minor increase in the model’s size.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\tAs depicted in Figure 4, the utilization of the SSL representation leads to rapid convergence, and we observe our model outperforming the baseline with only a few epochs of training. \n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tDespite reducing the size of the encoder by 80% (specifically, decreasing the number of encoder layers from 12 to only 2), we find that our model continues to outperform the baseline by a significant margin. \n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tFinally, Figure 5 illustrates the attention scores originating from our fusion layer based on cross-attention. It is interesting to note that the attention ultimately focused solely on capturing the nearby information despite having the ability to access the entire representation. This implies that the local context holds greater significance in the context of this fusion. In addition, this cross-attention block can now be utilized to obtain the alignment between both representations.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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To know more about Sony Research India\u2019s research publications, visit the \u2018Publications\u2019 section on our \u2018Open Innovation\u2019s page:<\/p>

Open Innovation with Sony R&D \u2013 Sony Research India<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Darshan Prabhu from the Content Analysis- Audio team summarises…<\/p>\n","protected":false},"author":1,"featured_media":11530,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"elementor_header_footer","format":"standard","meta":{"footnotes":""},"categories":[22,17],"tags":[],"class_list":["post-11496","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-all-blogs","category-technology","entry"],"yoast_head":"\nDecoding \u2018Efficient infusion of self-supervised representations in Automatic Speech Recognition\u2019 - Sony Research India<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/whiteriversmediasolutions.com\/Sony\/decoding-efficient-infusion-of-self-supervised-representations-in-automatic-speech-recognition\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" 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