[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82505-en":3,"doc-seo-82505-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82505,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1782698725881665579",8,"Research & Report","Speech Playground: An Interactive Tool for Speech Analysis and Comparison","Speech Playground is an interactive tool for visualizing and comparing speech features across utterances. It streamlines research workflows that otherwise require Python encoders, alignment code, and custom plotting when working with modern deep-learning representations. The system combines a Python FastAPI backend with a SvelteKit web frontend, supporting continuous, discrete, and variable-length representations. It includes TextGrid handling and forced alignment, plus configurable distance and alignment settings for visual and auditory comparison. Intended for speech research, representation validation, and CAPT experimentation.","Speech Playground: An Interactive Tool for Speech Analysis and Comparison  \nStephen McIntosh  1, Daisuke Saito  1 , Nobuaki Minematsu  1  \n1 The University of Tokyo, Japan  \n{smcintosh,[mine](mine}@gavo.t.u-tokyo.ac.jp)[}](mine}@gavo.t.u-tokyo.ac.jp)[@gavo.t.u-tokyo.ac.jp](mine}@gavo.t.u-tokyo.ac.jp)  \narXiv :2607 .004 18v 1 [ cs .CL] 1 Jul 2026  \nAbstract  \nThis paper presents Speech Playground, an interactive speech visualization and comparison tool. While existing tools such as Praat are excellent, it can be cumbersome to integrate them with modern deep learning representations and use them for comparison. Speech Playground addresses this by combining a Python backend with a web-based frontend for interactive exploration of multiple feature types, including continuous, discrete, and variable-length representations. It includes TextGrid and forced alignment support together with configurable distance and alignment settings for visual and auditory comparison. Speech Playground is intended for use in speech research, representation validation, and computer-aided pronunciation training (CAPT)-oriented experimentation.  \nIndex Terms: speech analysis, utterance comparison, CAPT  \n1. Introduction  \nInteractive tools for speech analysis such as Praat are widely used in speech research and are also useful for speech feedback in CAPT settings. However, recent deep-learning-based speech research has produced many different representations such as self-supervised or articulatory features. Comparing these requires Python-based encoders, alignment code, and ad-hoc visualization scripts, which is cumbersome.  \nIn this paper, we present Speech Playground1 , an extensible interactive tool for visualization of speech features and utterance comparison. Speech Playground has two modes: Analysis for single-track visualization (Figure 1) and Diff (Figure 2) for utterance comparison.  \nSpeech Playground provides a single interactive environment in which users can compare speech encoders, continuous, discrete, and variable-length representations, and alternative distance and alignment settings on the same utterance pair. We envision the following use cases: (1) Speech research using speech features unavailable in other tools and Diff mode to explain variation in speech with respect to a reference; (2) Representation validation by checking whether a representation captures a specific contrast or behaves consistently with the audio; and (3) CAPT-oriented experimentation using Diff mode to show where and how model speech and learner speech are different.  \n2. Overview  \n2.1. Architecture  \nSpeech Playground comprises three components:  \n1 [https://github.com/stephenmac7/](https://github.com/stephenmac7/)[ ](https://github.com/stephenmac7/)speech-playground  \nFigure 1: Sample viewer with TextGrid annotation and phonological vector tiers [1]. Positive and negative activations are shaded purple and orange, respectively.  \nThe frontend is a SvelteKit application that provides two primary modes: Analysis, for examining a single utterance, and Diff, for aligning and comparing two utterances. WaveSurfer.js is used for waveform visualization. IndexedDB is used to manage and persist uploaded recordings and metadata such as transcription and TextGrid files.  \nThe backend is a FastAPI (Python) server exposing speech processing endpoints including encoding, segmentation, and alignment, lazily loading models on demand for fast startup and iteration.  \nThe speech-processing library provides a uniform interface over feature extractors, called encoders. Each encoder maps waveforms to a sequence of continuous frameor segment-level representations. Built-in encoders include SSL, articulatory, phonological-feature, and segmental representations, including SSL-derived variable-length representations such as ZeroSyl [2] . Representations can optionally be transformed into discrete units or grouped into coarser variablelength segments.  \nThe speech-processing library also","cbCaipyO6IOjDg9O","https://ap.wps.com/l/cbCaipyO6IOjDg9O","pdf",550675,1,2,"English","en",105,"# Introduction\n# Overview\n## Architecture\n## Components","[{\"question\":\"What problem does Speech Playground address compared with tools like Praat?\",\"answer\":\"Speech Playground tackles the integration burden of aligning deep-learning-based speech representations with encoding and visualization workflows, which often requires Python encoders, alignment code, and ad-hoc scripts.\"},{\"question\":\"What capabilities does Speech Playground provide for comparing speech representations?\",\"answer\":\"It supports multiple feature types—continuous, discrete, and variable-length representations—and allows users to switch distance and alignment settings on the same utterance pair, including DTW and segment-based alignment approaches.\"},{\"question\":\"How does Speech Playground support research and CAPT-oriented use cases?\",\"answer\":\"It provides Diff mode to explain variation relative to a reference, validate whether a representation captures a contrast and behaves consistently with audio, and highlight where model speech and learner speech differ for pronunciation training 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problem does Speech Playground address compared with tools like Praat?","Question",{"text":74,"@type":75},"Speech Playground tackles the integration burden of aligning deep-learning-based speech representations with encoding and visualization workflows, which often requires Python encoders, alignment code, and ad-hoc scripts.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What capabilities does Speech Playground provide for comparing speech representations?",{"text":79,"@type":75},"It supports multiple feature types—continuous, discrete, and variable-length representations—and allows users to switch distance and alignment settings on the same utterance pair, including DTW and segment-based alignment approaches.",{"name":81,"@type":72,"acceptedAnswer":82},"How does Speech Playground support research and CAPT-oriented use cases?",{"text":83,"@type":75},"It provides Diff mode to explain variation relative to a reference, validate whether a representation captures a contrast and 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