「recognition」の検索結果

FREE voice recognition using Groq's complimentary Whisper API.

日本語の概要は準備中です。原文の説明を表示しています。

Modelo de reconhecimento de fala de propósito geral da OpenAI. Suporta 99 idiomas, transcrição, tradução para inglês e identificação de idioma. Seis tamanhos de modelo, de tiny (39M parâmetros) a large (1550M parâmetros). Use para speech-to-text, transcrição de podcasts ou processamento de áudio multilíngue. Melhor para ASR robusto e multilíngue.

日本語の概要は準備中です。原文の説明を表示しています。

Load this skill whenever the project must support speech input or voice control — dictation, "click by voice" grammars, Dragon NaturallySpeaking, Voice Control (iOS/macOS), Voice Access (Android), or any interface where users activate controls by speaking. Under no circumstances let an aria-label override a visible label without keeping the visible text intact and in order. Absolutely always ensure the accessible name contains the visible label, and load alongside keyboard/SKILL.md since speech tools commonly emulate keyboard and pointer input.

日本語の概要は準備中です。原文の説明を表示しています。

Brand mention and authority scanner for AI visibility. Analyzes brand presence across platforms that AI models rely on for entity recognition and citation decisions. Produces a Brand Authority Score (0-100) with platform-specific recommendations.

日本語の概要は準備中です。原文の説明を表示しています。

Build call automation workflows with Azure Communication Services Call Automation Java SDK. Use when implementing IVR systems, call routing, call recording, DTMF recognition, text-to-speech, or AI-powered call flows.

日本語の概要は準備中です。原文の説明を表示しています。

Genogram work: Recognizing and reflecting on family relationship patterns. Multigenerational perspective, genogram symbols, pattern recognition, and resources in family history.

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user asks to "optimize entity presence", reconcile an entity identity, or update canonical Knowledge Graph facts; audits and maintains machine-facing identity, sameAs, schema, disambiguation, and AI-recognition evidence through the entities registry. Not for page-level AI-citation readiness - use geo-content-optimizer; not for human-facing brand canon - use narrative-registry. 实体注册/知识图谱

日本語の概要は準備中です。原文の説明を表示しています。

You are an expert AI-powered code review specialist combining automated static analysis, intelligent pattern recognition, and modern DevOps practices. Leverage AI tools (GitHub Copilot, Qodo, GPT-5, C

日本語の概要は準備中です。原文の説明を表示しています。

Master binary analysis patterns including disassembly, decompilation, control flow analysis, and code pattern recognition. Use when analyzing executables, understanding compiled code, or performing...

日本語の概要は準備中です。原文の説明を表示しています。

Azure Speech to Text REST API for short audio (Python). Use for simple speech recognition of audio files up to 60 seconds without the Speech SDK.

日本語の概要は準備中です。原文の説明を表示しています。

Build call automation workflows with Azure Communication Services Call Automation Java SDK. Use when implementing IVR systems, call routing, call recording, DTMF recognition, text-to-speech, or AI-...

日本語の概要は準備中です。原文の説明を表示しています。

Azure AI Text Analytics SDK for sentiment analysis, entity recognition, key phrases, language detection, PII, and healthcare NLP. Use for natural language processing on text.

日本語の概要は準備中です。原文の説明を表示しています。

Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.

日本語の概要は準備中です。原文の説明を表示しています。

You are an expert AI-powered code review specialist combining automated static analysis, intelligent pattern recognition, and modern DevOps practices. Leverage AI tools (GitHub Copilot, Qodo, GPT-5, C

日本語の概要は準備中です。原文の説明を表示しています。

Azure AI Text Analytics SDK for sentiment analysis, entity recognition, key phrases, language detection, PII, and healthcare NLP. Use for natural language processing on text.

日本語の概要は準備中です。原文の説明を表示しています。

The 10-second design audit. Look at any design and name what's working and what's not within seconds. Trains rapid pattern recognition for hierarchy, spacing, type, and color. Use when evaluating designs quickly, giving first-impression feedback, or building perception speed.

日本語の概要は準備中です。原文の説明を表示しています。

Implement computer vision features including text recognition (OCR), face detection, barcode scanning, image segmentation, object tracking, and document scanning in iOS apps. Covers both the modern Swift-native Vision API (iOS 16+) and legacy VNRequest patterns, VisionKit DataScannerViewController for live camera scanning, and VNCoreMLRequest for custom model inference. Use when adding OCR, barcode scanning, face detection, or custom Core ML model inference with Vision.

日本語の概要は準備中です。原文の説明を表示しています。

Add drawings, shapes, and a consistent markup experience using PaperKit. Use when integrating PaperMarkupViewController for markup editing, adding shape recognition, working with PaperMarkup data models, embedding markup tools in document editors, or building annotation features that need the system-standard markup toolbar. New in iOS 26.

日本語の概要は準備中です。原文の説明を表示しています。

Tokenize, tag, and analyze natural language text using Apple's NaturalLanguage framework and translate between languages with the Translation framework. Use when adding language identification, sentiment analysis, named entity recognition, part-of-speech tagging, text embeddings, or in-app translation to iOS/macOS/visionOS apps.

日本語の概要は準備中です。原文の説明を表示しています。

Access accelerometer, gyroscope, magnetometer, pedometer, and activity-recognition data using CoreMotion. Use when reading device sensor data, counting steps, detecting user activity (walking/running/driving), tracking altitude changes, or implementing motion-based interactions in iOS/watchOS apps.

日本語の概要は準備中です。原文の説明を表示しています。

Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flow with sub-800ms latency while handling interruptions, background noise, and emotional nuance. This skill covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency, most natural) and pipeline (STT→LLM→TTS, more control, easier to debug). Key insight: latency is the constraint. Hu

日本語の概要は準備中です。原文の説明を表示しています。

Azure Speech to Text REST API for short audio (Python). Use for simple speech recognition of audio files up to 60 seconds without the Speech SDK.

日本語の概要は準備中です。原文の説明を表示しています。

Azure AI Text Analytics SDK for sentiment analysis, entity recognition, key phrases, language detection, PII, and healthcare NLP. Use for natural language processing on text.

日本語の概要は準備中です。原文の説明を表示しています。

Specialized reverse engineering analysis workflows for binary analysis, pattern recognition, and vulnerability assessment

日本語の概要は準備中です。原文の説明を表示しています。