Skill UI

A comprehensive Java SDK for building robust, server-side call automation workflows using Azure Communication Services (ACS). It allows developers to implement sophisticated IVR systems, dynamic call routing, interactive media playback (Text-to-Speech, audio files), speech and DTMF recognition, call recording management, and participant handling. Ideal for building sophisticated customer service bots or automated notification systems.

A comprehensive Java SDK for building robust, server-side call automation workflows using Azure Communication Services (ACS). It allows developers to implement sophisticated IVR systems, dynamic call routing, interactive media playback (Text-to-Speech, audio files), speech and DTMF recognition, call recording management, and participant handling. Ideal for building sophisticated customer service bots or automated notification systems.

A Python implementation utilizing the Azure Speech-to-Text REST API for transcribing short audio files (up to 60 seconds). This method avoids the need for the dedicated Speech SDK, allowing developers to perform speech recognition simply by making HTTP POST requests. It supports both simple and detailed response formats and recommends chunked transfer for lower latency.

A comprehensive guide for developers planning Deepgram SDK version upgrades (v3/v4 to v5) and model migrations (Nova-2 to Nova-3). This resource details breaking changes, provides side-by-side API comparisons, and includes automated validation scripts to ensure smooth integration of new features and improved speech recognition capabilities.

This guide details how to set up the Speak language learning platform integration. It covers installing necessary SDKs (Speak SDK, OpenAI), configuring API keys, and establishing the connection required for sophisticated AI-powered tutoring. Use this when building applications that require real-time speech recognition, pronunciation feedback, and multilingual language support.

OpenAI’s Whisper provides robust multilingual speech-to-text transcriptions, translations to English, and language detection across 99 languages, suitable for podcasts, meetings, and noisy audio workflows with CPU/GPU acceleration and batch or streaming modes.

Execute comprehensive, asynchronous transcription workflows using AssemblyAI. This tool handles file upload, queues jobs, and provides advanced audio intelligence features. Capabilities include speaker diarization, sentiment analysis, named entity recognition (NER), topic categorization (IAB), content safety moderation, and sensitive PII redaction. Ideal for processing meeting recordings, interviews, and large volumes of spoken content.

This debug bundle provides comprehensive guidance for integrating the Wispr Flow voice-to-text API. It covers best practices for various integration methods, such as low-latency WebSocket streaming and standard REST API calls. It is essential for developers building sophisticated voice-powered applications, dictation tools, or any system requiring robust speech recognition and error handling.

A comprehensive guide for integrating the Wispr Flow voice-to-text API using webhooks. This skill covers best practices for real-time speech recognition, including low-latency WebSocket streaming and robust REST API calls. It is essential for developers building voice-powered applications, dictation tools, or any system requiring advanced audio processing and context-aware transcription.

This comprehensive guide helps researchers position their work in Automatic Speech Recognition (ASR) and Spoken Language Understanding (SLU) for the IEEE ASRU workshop. It covers conference fit assessment, manuscript re-framing strategies, understanding venue-specific evidence requirements (e.g., decoding, robustness analysis), and differentiating the contribution from related fields like Interspeech or SLT. Use this tool for submission strategy and academic positioning.

This skill guides authors on how to strategically position their research for the INTERSPEECH conference. It covers core topics such as speech recognition, synthesis, paralinguistics, and audio-language modeling. Use it to re-frame manuscripts from journal or arXiv styles into a selective CS conference narrative, ensuring the work meets the rigorous standards of audio-specific evaluation, datasets, and error analysis expected by the field.

Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript across browsers and server-side runtimes like Node.js, Bun, and Deno, enabling text analysis, image classification, speech recognition, and multimodal AI applications without a Python backend.