{"id":13889,"date":"2025-09-05T06:38:55","date_gmt":"2025-09-05T06:38:55","guid":{"rendered":"https:\/\/whiteriversmediasolutions.com\/Sony\/summarizing-kanite-kolmogorov-arnold-networks-for-ite-estimation-copy\/"},"modified":"2025-09-05T07:25:24","modified_gmt":"2025-09-05T07:25:24","slug":"laspa-breaking-language-barriers-in-speaker-recognition-with-prefix-tuned-cross-attention","status":"publish","type":"post","link":"https:\/\/whiteriversmediasolutions.com\/Sony\/laspa-breaking-language-barriers-in-speaker-recognition-with-prefix-tuned-cross-attention\/","title":{"rendered":"LASPA: Breaking Language Barriers in Speaker Recognition with Prefix-Tuned Cross-Attention"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"13889\" class=\"elementor elementor-13889\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-cd44eb5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"cd44eb5\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-9f11b70\" data-id=\"9f11b70\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-215a70e elementor-widget elementor-widget-heading\" data-id=\"215a70e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">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<section class=\"elementor-section elementor-top-section elementor-element elementor-element-28dc161 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"28dc161\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-63cf269\" data-id=\"63cf269\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6837436 elementor-widget elementor-widget-heading\" data-id=\"6837436\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">LASPA: Breaking Language Barriers in Speaker Recognition with Prefix-Tuned Cross-Attention<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9bd1630 elementor-widget elementor-widget-text-editor\" data-id=\"9bd1630\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tAditya Srinivas Menon*, Raj Prakash Gohil*, Kumud Tripathi, Pankaj Wasnik\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7a034cb elementor-hidden-desktop elementor-hidden-tablet elementor-hidden-mobile elementor-widget elementor-widget-text-editor\" data-id=\"7a034cb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>30<sup>th<\/sup> September 2024<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-454e546 elementor-widget elementor-widget-text-editor\" data-id=\"454e546\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Raj Prakash Gohil summarizes paper titled <a href=\"https:\/\/arxiv.org\/abs\/2506.02083\">LASPA: Breaking Language Barriers in Speaker Recognition with Prefix-Tuned Cross-Attention<\/a> co-authored by Aditya Srinivas Menon, Raj Prakash Gohil, Kumud Tripathi and Pankaj Wasnik accepted at the <a href=\"https:\/\/www.interspeech2025.org\/home\">26th edition of the Interspeech 2025 Conference | August 17-21, 2025.<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f0a3e28 elementor-widget elementor-widget-text-editor\" data-id=\"f0a3e28\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h4><strong>Introduction<\/strong><\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9202657 elementor-widget elementor-widget-text-editor\" data-id=\"9202657\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Imagine a world where your voice unlocks your devices, regardless of the language you speak. Speaker recognition systems are everywhere\u2014from smart assistants to security systems. But what happens when you switch languages? Traditional systems often stumble, confusing language-induced changes in your voice of a different person.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-682ac84 elementor-widget elementor-widget-text-editor\" data-id=\"682ac84\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>LASPA<\/strong>: a novel approach that disentangles speaker identity from language, enabling robust, language-agnostic speaker recognition\u2014even when you switch between languages.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f6c8504 elementor-widget elementor-widget-text-editor\" data-id=\"f6c8504\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h4><strong>The Challenge: Language Entanglement in Speaker Recognition<\/strong><\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c793aaa elementor-widget elementor-widget-text-editor\" data-id=\"c793aaa\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Speaker recognition models extract \u201cembeddings\u201d\u2014compact representations of your voice. But these embeddings often mix up two things:<\/p><ul><li><strong>Speaker traits<\/strong> (your unique vocal anatomy, timbre, etc.)<\/li><li><strong>Linguistic traits<\/strong> (accent, phonetic structure, intonation)<\/li><\/ul><p>When you speak a different language, your voice changes\u2014not because you\u2019re a different person, but because the language demands different sounds and rhythms. Traditional models can\u2019t always tell the difference, leading to errors.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4364049 elementor-widget elementor-widget-text-editor\" data-id=\"4364049\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h4><strong>The Solution: LASPA\u2019s Disentanglement Strategy<\/strong><\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5f41fc9 elementor-widget elementor-widget-text-editor\" data-id=\"5f41fc9\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>LASPA (Language Agnostic Speaker Disentanglement with Prefix-Tuned Cross-Attention) introduces a <strong>joint learning strategy<\/strong> that separates speaker and language information.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f457432 elementor-widget elementor-widget-text-editor\" data-id=\"f457432\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>Key Innovations<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0efe63b elementor-widget elementor-widget-text-editor\" data-id=\"0efe63b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ol><li><strong>Prefix-Tuned Cross-Attention<\/strong>:Instead of updating all model parameters, LASPA uses \u201cprefix vectors\u201d to guide attention,efficiently fusing speaker and language features.<\/li><li><strong>Dual Encoders<\/strong>:<ul><li><strong>Speaker Encoder<\/strong>: Extracts speaker-specific features.<\/li><li><strong>Language Encoder<\/strong>: Extracts language-specific features.<\/li><\/ul><\/li><li><strong>Prefix-Tuners<\/strong>:<ul><li>Facilitate focused interaction between speaker and language features.<\/li><li>Enable the model to \u201cpay attention\u201d to the right information for each task.<\/li><\/ul><\/li><li><strong>Multi-Task Loss Functions<\/strong>:<ul><li><strong>AAM Softmax<\/strong>: For speaker classification.<\/li><li><strong>Negative Log Likelihood (NLL)<\/strong>: For language classification.<\/li><li><strong>Mean Absolute Pearson\u2019s Correlation (MAPC)<\/strong>: To encourage disentanglement.<\/li><li><strong>Mean Squared Error (MSE)<\/strong>: For reconstructing the input spectrogram.<\/li><\/ul><\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-36de7e0 elementor-widget elementor-widget-text-editor\" data-id=\"36de7e0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h4><strong>How LASPA Works: Architecture Overview<\/strong><\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a7d1e72 elementor-widget elementor-widget-image\" data-id=\"a7d1e72\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" data-src=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/elementor\/thumbs\/blog1-rbaqmn2w22egtz1poq25hg7172qt8aic0l6l3egdue.png\" title=\"blog1\" alt=\"blog\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" class=\"lazyload\" style=\"--smush-placeholder-width: 700px; --smush-placeholder-aspect-ratio: 700\/335;\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-79dee33 elementor-widget elementor-widget-text-editor\" data-id=\"79dee33\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>Step-by-step:<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a7fbcda elementor-widget elementor-widget-text-editor\" data-id=\"a7fbcda\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ol><li><strong>Input<\/strong>: Audio waveform is converted to a mel-spectrogram.<\/li><li><strong>Speaker &amp; Language Encoders<\/strong>: Extract separate embeddings.<\/li><li><strong>Prefix-Tuners<\/strong>:<ul><li>Two cross-feature prefix-tuners blend speaker and language information using multi-head attention.<\/li><\/ul><\/li><li><strong>Decoder<\/strong>:<ul><li>Concatenated embeddings are used to reconstruct the original mel-spectrogram.<\/li><\/ul><\/li><li><strong>Training<\/strong>:<ul><li>Multiple loss functions ensure the model learns to separate speaker and language features.<\/li><\/ul><\/li><li><strong>Inference<\/strong>:<ul><li>Only the speaker encoder is used, ensuring fast and language-agnostic speaker embedding extraction.<\/li><\/ul><\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-52b6378 elementor-widget elementor-widget-text-editor\" data-id=\"52b6378\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>Datasets Used:<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c25b84d elementor-widget elementor-widget-text-editor\" data-id=\"c25b84d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul>\n \t<li><strong>VoxCeleb1, VoxCeleb2<\/strong>: Large-scale speaker recognition datasets.<\/li>\n \t<li><strong>VoxSRC 2020\/2021<\/strong>: Multilingual speaker recognition challenges.<\/li>\n \t<li><strong>NISP-B<\/strong>: Multilingual dataset with English, Hindi, Kannada, Malayalam, Tamil, Telugu.<\/li>\n \t<li><strong>DISPLACE<\/strong>: For speaker diarization (who spoke when).<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-671b4b1 elementor-widget elementor-widget-text-editor\" data-id=\"671b4b1\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>Performance Metrics<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4747f5b elementor-widget elementor-widget-text-editor\" data-id=\"4747f5b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul><li><strong>EER (Equal Error Rate)<\/strong>: Lower is better.<\/li><li><strong>minDCF (Minimum Decision Cost Function)<\/strong>: Lower is better.<\/li><li><strong>SLR Accuracy<\/strong>: Lower means less language information in speaker embeddings.<\/li><li><strong>Cosine Similarity<\/strong>: Higher means more consistent speaker embeddings across languages.<\/li><li><strong>DER (Diarization Error Rate)<\/strong>: Lower is better.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7862fb2 elementor-widget elementor-widget-text-editor\" data-id=\"7862fb2\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>RESULTS:<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3e2a4d9 elementor-widget elementor-widget-image\" data-id=\"3e2a4d9\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" data-src=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/elementor\/thumbs\/blog1-results-rbaqv99nx8yyepclgabqx8xhvq1d69yahjk5l5mrna.png\" title=\"blog1-results\" alt=\"blog1-results\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" class=\"lazyload\" style=\"--smush-placeholder-width: 800px; --smush-placeholder-aspect-ratio: 800\/575;\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6778629 elementor-widget elementor-widget-image\" data-id=\"6778629\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"613\" height=\"592\" src=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/09\/blog1-results-1.png\" class=\"attachment-medium_large size-medium_large wp-image-13904\" alt=\"\" srcset=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/09\/blog1-results-1.png 613w, https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/09\/blog1-results-1-300x290.png 300w\" sizes=\"(max-width: 613px) 100vw, 613px\" style=\"width:100%;height:96.57%;max-width:613px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7ed0fea elementor-widget elementor-widget-text-editor\" data-id=\"7ed0fea\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h4><strong>Why Does LASPA Matter?<\/strong><\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d95d9a3 elementor-widget elementor-widget-text-editor\" data-id=\"d95d9a3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul><li><strong>Language Agnostic<\/strong>: Recognizes you, not your language.<\/li><li><strong>Efficient<\/strong>: Prefix-tuners add just 1.16% to model parameters.<\/li><li><strong>Generalizes Well<\/strong>: Works on unseen languages and challenging multilingual datasets.<\/li><li><strong>Better Diarization<\/strong>: Outperforms baselines in \u201cwho spoke when\u201d tasks.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1246231 elementor-widget elementor-widget-text-editor\" data-id=\"1246231\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h4><strong>Conclusion &amp; Future Directions<\/strong><\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-424bb7e elementor-widget elementor-widget-text-editor\" data-id=\"424bb7e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>LASPA is a leap forward for speaker recognition in our multilingual world. By disentangling speaker and language information using prefix-tuned cross-attention, it delivers robust, efficient, and language-agnostic performance.<\/p><p><strong>What\u2019s next?<\/strong> The authors plan to further refine prefix-tuning and explore its applications in speaker verification and related tasks.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cd9e70e elementor-widget elementor-widget-text-editor\" data-id=\"cd9e70e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h4><strong>Citation<\/strong><\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-487414c elementor-widget elementor-widget-text-editor\" data-id=\"487414c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>@misc{menon2025laspalanguageagnosticspeaker,<\/p><p>\u00a0\u00a0\u00a0\u00a0\u00a0 title={LASPA: Language Agnostic Speaker Disentanglement with Prefix-Tuned Cross-Attention},<\/p><p>\u00a0\u00a0\u00a0\u00a0\u00a0 author={Aditya Srinivas Menon and Raj Prakash Gohil and Kumud Tripathi and Pankaj Wasnik},<\/p><p>\u00a0\u00a0\u00a0\u00a0\u00a0 year={2025},<\/p><p>\u00a0\u00a0\u00a0\u00a0\u00a0 eprint={2506.02083},<\/p><p>\u00a0\u00a0\u00a0\u00a0\u00a0 archivePrefix={arXiv},<\/p><p>\u00a0\u00a0\u00a0\u00a0\u00a0 primaryClass={cs.SD},<\/p><p>\u00a0\u00a0\u00a0\u00a0\u00a0 url={https:\/\/arxiv.org\/abs\/2506.02083},<\/p><p>}<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0362925 elementor-hidden-desktop elementor-hidden-tablet elementor-hidden-mobile elementor-widget elementor-widget-text-editor\" data-id=\"0362925\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>In most of the cases, it has been found that Content Driven sessions outperform the time driven sessions. The results are obtained on 6 baselines: STAMP, NARM, GRU4Rec, CD-HRNN, Tr4Rec on datasets like Movielens (Movies), GoodRead Book, LastFM (Music), Amazon (e-commerce).<\/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<section class=\"elementor-section elementor-top-section elementor-element elementor-element-c0518a1 elementor-hidden-desktop elementor-hidden-tablet elementor-hidden-mobile elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c0518a1\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-b15be70\" data-id=\"b15be70\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-55dd72b\" data-id=\"55dd72b\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e06d72d elementor-widget elementor-widget-image\" data-id=\"e06d72d\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"512\" height=\"322\" data-src=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2024\/02\/19th-Cover-Image-2.png\" class=\"attachment-full size-full wp-image-11786 lazyload\" alt=\"\" data-srcset=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2024\/02\/19th-Cover-Image-2.png 512w, https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2024\/02\/19th-Cover-Image-2-300x189.png 300w\" data-sizes=\"(max-width: 512px) 100vw, 512px\" style=\"--smush-placeholder-width: 512px; --smush-placeholder-aspect-ratio: 512\/322;width:100%;height:62.89%;max-width:512px\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" \/>\t\t\t\t\t\t\t\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<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-fd52b32\" data-id=\"fd52b32\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\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<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9b69060 elementor-hidden-desktop elementor-hidden-tablet elementor-hidden-mobile elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9b69060\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-cfbe302\" data-id=\"cfbe302\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6d045fb elementor-widget elementor-widget-text-editor\" data-id=\"6d045fb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tThe introduced modules and techniques help the proposed method to align known class\nrepresentations effectively so that it can detect the unknown objects accurately. To validate\nthis, we carried out extensive experiments &#038; ablation studies and found that the proposed\nmethod outperforms existing SOTA methods with significant improvement on the MS-COCO\n&#038; PASCAL VOC dataset for the OSOD task.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f97c4c4 elementor-widget elementor-widget-text-editor\" data-id=\"f97c4c4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tTo know more about the paper, visit: <a href=\"https:\/\/openaccess.thecvf.com\/content\/WACV2024\/papers\/Sarkar_Open-Set_Object_Detection_by_Aligning_Known_Class_Representations_WACV_2024_paper.pdf\" target=\"_blank\" rel=\"noopener\">Open-Set Object Detection by Aligning Known Class\nRepresentations (thecvf.com)<\/a>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9e2f9cc elementor-widget elementor-widget-text-editor\" data-id=\"9e2f9cc\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tTo know more about Sony Research India\u2019s Research Publications, visit the \u2018Publications\u2019\nsection on our \u2018Open Innovation\u2019s page: <a href=\"https:\/\/www.sonyresearchindia.com\/open-innovation\/\" target=\"_blank\" rel=\"noopener\">Open Innovation with Sony R&amp;D \u2013 Sony Research\nIndia<\/a>\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":"<p>Raj Prakash Gohil summarizes paper titled LASPA&#8230; <\/p>\n","protected":false},"author":1,"featured_media":13914,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"elementor_header_footer","format":"standard","meta":{"footnotes":""},"categories":[22,17],"tags":[],"class_list":["post-13889","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-all-blogs","category-technology","entry"],"yoast_head":"\n<title>LASPA: Breaking Language Barriers in Speaker Recognition with Prefix-Tuned Cross-Attention - 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\/laspa-breaking-language-barriers-in-speaker-recognition-with-prefix-tuned-cross-attention\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"LASPA: Breaking 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