Identified spoken words

100%

Identified spoken words

✓ Published0🌍 Public

Last edited May 4, 2019

Created on Apr 16, 2019

This example shows identified spoken words as they are captured through a browser’s speech recognition API, displaying the transcript in a form input. The code uses the `SpeechRecognitionService` to activate listening, with callbacks for no-match and unrecognized results that log or alert the user. It relies on the native Web Speech API, invoked via `startRecognition()`, and handles errors by catching exceptions and displaying an alert. The visualization itself is minimal, focusing on the interaction between voice input and the text field, without any charting or rendering libraries.

AI-generated description

Similar vizzes

Loading thumbnail…

Utter word in course language with Web Speech API

This example demonstrates how to pronounce a course word aloud in the user’s current language using the Web Speech API. It retrieves the user’s course language code and selects a matching voice from `SpeechSynthesisService.getVoices()`, then calls `SpeechSynthesisService.speak()` with the word, setting rate, pitch, and volume. The code relies on the browser’s speech synthesis capabilities to deliver audio feedback directly from the interface.

VVeronicaM

63% match

Loading thumbnail…

Gist e6556a05221bd9819d98cc5a5363a364

This example shows a live speech-to-text service that streams raw PCM audio from an HTTP request body into Microsoft Azure's Speech SDK for real-time transcription. The Python code implements a custom `PullAudioInputStream` callback that incrementally reads bytes from the Gunicorn request stream and writes them into a `BytesIO` buffer, while the falcon endpoint's `on_post` method triggers recognition. As Azure processes the audio, callback functions capture recognized and intermediate results, which are accumulated into lists along with timing and confidence data. The implementation uses the `speechsdk.audio` module, datetime for soft timeouts, and JSON parsing to extract transcription details from recognition events.

CCheckroth

53% match

Loading thumbnail…

Gist 2a036239a620a1b1a6ee

This example demonstrates a stopword filtering visualization. It shows a collection of common English words—"the," "a," "in," "of," and "as"—rendered as a simple text list, visually distinguishing them from a larger text corpus. The implementation uses an XML-based data structure to define the stopword set, which is then parsed and displayed. The code relies on standard browser APIs for XML parsing and DOM manipulation, presenting the filtered words in a clean, readable format. The visualization makes no attempt at complex layout or animation, instead offering a straightforward, static view of which terms are excluded from analysis, with the underlying data sourced directly from the embedded XML file.

CCliffordAnderson

53% match

Loading thumbnail…

send image to google vision endpoint

This example shows a client-side web application that sends a canvas-rendered image to a Google Vision endpoint and displays the recognized labels in the page. It first captures the canvas content as a PNG dataURL using `toDataURL('image/png')`, then posts that data along with a selected course language value via `fetch` to the server. The response is an array of words, which the code maps into HTML list items showing each recognized term and its associated language. The rendering uses plain DOM manipulation with `innerHTML`, and the request/response cycle relies on standard JavaScript Promises.

VVeronicaM

52% match

Loading thumbnail…

Example from Programming4Humanists

This example visualizes word frequency in a text by processing the poem’s lines and filtering out common stopwords. It shows a ranked list of words by their occurrence count, displayed in descending order. The code uses XQuery 3.0 with functions like `fn:tokenize`, `fn:distinct-values`, and `fn:count` to compute frequencies, while reading the source text and stopword list from gist URLs. The rendering approach is a simple textual output of word elements, each carrying a `frequency` attribute, rather than a graphical chart.

CCliffordAnderson

50% match

Loading thumbnail…

<title>Oral History Transcript

This visualization parses an oral history transcript encoded in TEI XML, displaying the dialogue between two speakers as a series of connected text blocks. It shows the conversational flow, with each speech act rendered as a separate node and the speaker identified by color-coded labels. The code uses D3.js’s `d3.forceSimulation` for layout and `d3.select` with data binding to manage the nodes and links, while the raw XML is loaded via `d3.xml` and traversed with DOM methods to extract speaker turns and text. The visualization highlights gaps in speech (e.g., `<gap n="00:00:28"/>`) by adding time-stamped ellipses, making the transcript’s pacing and pauses explicit.

CCliffordAnderson

50% match

Loading thumbnail…

Multi-Line Voronoi.find() w/ speech data

This example visualizes the sentiment trajectory of political speech transcripts as a multi-line chart, with each line representing a different speech and the x-axis showing progress through the text while the y-axis plots sentiment values. A Voronoi tessellation computed via `d3.voronoi()` enables interactive highlighting; the code uses the `voronoi.find()` method to identify the nearest line to the mouse cursor, though the mouseover handler is currently commented out. The visualization renders with SVG, uses Vue as its framework, and reads speech data from a CSV file. A checkbox toggles visibility of the Voronoi diagram overlay.

BBBischof

49% match

Loading thumbnail…

Gist bfd5f48411d371e896e1

This visualization maps the speech patterns of 2016 U.S. presidential candidates by scraping and parsing transcripts from the blog What The Folly. The author uses Bash commands with `wget`, `grep`, `sed`, and `awk` to extract article text, then organizes files by candidate. The resulting dataset supports comparative analysis of candidate rhetoric, though the code shown focuses on data collection and file management rather than visual rendering, with no charting libraries or display techniques specified.

BBBischof

48% match