{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_verbaly","slug":"verbaly","name":"Verbaly","type":"product","url":"https://www.verbaly.ai","page_url":"https://unfragile.ai/verbaly","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_verbaly__cap_0","uri":"capability://data.processing.analysis.real.time.voice.analysis.with.speech.quality.metrics","name":"real-time voice analysis with speech quality metrics","description":"Processes live audio input during user speech to extract and measure acoustic features including speech rate (words per minute), pause duration, filler word frequency (um, uh, like), and clarity markers. Uses signal processing pipelines to detect prosodic patterns and phonetic clarity in real-time, likely leveraging WebRTC for browser-based audio capture and streaming to backend speech analysis models that compute metrics against configurable thresholds for immediate feedback delivery.","intents":["I want to know my speaking pace and whether I'm talking too fast or too slow","I need to identify and count filler words I use unconsciously during practice","I want real-time alerts when my speech clarity drops or I'm mumbling","I need to measure my improvement in pacing and filler word reduction over multiple practice sessions"],"best_for":["professionals preparing for presentations who want quantitative feedback on delivery mechanics","non-native English speakers working on clarity and pace","interview candidates practicing verbal communication under time pressure"],"limitations":["Accuracy of filler word detection depends on audio quality and background noise — may produce false positives in noisy environments","Real-time processing latency (likely 500ms-2s) means feedback is slightly delayed, not truly instantaneous","Metrics are acoustic-only — cannot assess content quality, logical flow, or persuasiveness of speech","No speaker diarization — cannot distinguish between user speech and background voices in multi-speaker scenarios"],"requires":["Modern web browser with WebRTC support (Chrome 25+, Firefox 22+, Safari 11+)","Microphone access with at least 16kHz sample rate","Stable internet connection for streaming audio to backend analysis service","JavaScript enabled for real-time UI updates"],"input_types":["audio stream (PCM, WAV, or browser-native audio format)","user-defined speech duration (typically 30 seconds to 10 minutes)"],"output_types":["structured metrics object: {speechRate: number, fillerWordCount: number, fillerWordList: string[], pauseDuration: number[], clarityScore: number}","real-time visual feedback (progress bar, metric updates)","actionable text recommendations"],"categories":["data-processing-analysis","speech-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_verbaly__cap_1","uri":"capability://text.generation.language.conversational.ai.speaking.partner.with.guided.practice.scenarios","name":"conversational ai speaking partner with guided practice scenarios","description":"Implements a multi-turn dialogue system where the AI takes on specific conversation roles (interviewer, audience member, client, etc.) and responds contextually to user speech input, creating realistic practice scenarios without requiring human partners. The system likely uses a large language model (GPT-based or similar) with prompt engineering to maintain character consistency, respond to speech content (transcribed via speech-to-text), and generate follow-up questions or objections that simulate real conversation dynamics.","intents":["I want to practice answering interview questions with an AI that responds naturally to my answers","I need to simulate a client presentation where the AI asks challenging follow-up questions","I want to practice difficult conversations (negotiations, feedback delivery) in a low-stakes environment","I need to practice explaining technical concepts to a non-technical audience represented by the AI"],"best_for":["job interview candidates preparing for behavioral and technical questions","sales professionals practicing pitch delivery and objection handling","executives preparing for board presentations or investor pitches","people with social anxiety who benefit from judgment-free practice with AI before human interaction"],"limitations":["AI responses, while contextual, lack true understanding of nuanced body language, tone interpretation, and emotional subtext that human evaluators would provide","Speech-to-text transcription errors can cause the AI to misunderstand user intent and generate off-topic responses, breaking conversation flow","Limited scenario customization — likely offers pre-built templates rather than fully user-defined conversation contexts","No memory across sessions — each practice starts fresh without learning user's specific weak areas"],"requires":["Speech-to-text API (likely Google Cloud Speech-to-Text, Azure Speech Services, or Whisper)","LLM API access (OpenAI GPT, Anthropic Claude, or proprietary model)","Text-to-speech synthesis for AI responses (optional but likely included for full conversational experience)","Minimum 2-3 second latency tolerance for AI response generation"],"input_types":["user speech (audio stream converted to text via STT)","scenario selection (interview, presentation, negotiation, etc.)","optional context parameters (industry, role, difficulty level)"],"output_types":["AI-generated conversational responses (text and synthesized speech)","conversation transcript with timestamps","feedback summary on response quality and relevance"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_verbaly__cap_2","uri":"capability://data.processing.analysis.speech.to.text.transcription.with.speaker.segmentation","name":"speech-to-text transcription with speaker segmentation","description":"Converts user audio input into text transcripts in real-time or post-recording, likely using a speech-to-text engine (Whisper, Google Cloud Speech-to-Text, or Azure Speech Services) with speaker segmentation to distinguish between user speech and any background audio. The transcription is timestamped and formatted to enable downstream analysis, feedback generation, and user review of what was actually said versus intended.","intents":["I want to see a transcript of what I said to review my word choice and phrasing","I need to identify exactly where I used filler words in my speech for targeted practice","I want to compare my intended message with what actually came out of my mouth","I need a searchable record of my practice sessions to track specific phrases or topics I struggle with"],"best_for":["users who benefit from visual reinforcement of their speech patterns","non-native speakers who want to review pronunciation and word choice","professionals creating documentation of their practice sessions"],"limitations":["Transcription accuracy varies with audio quality, accent, and background noise — typically 85-95% accuracy depending on STT engine","Real-time transcription introduces 1-3 second latency before text appears, potentially disrupting user focus during active speech","Homophone confusion (their/there, to/too) and proper noun capitalization errors require manual correction","No semantic understanding — transcription is word-for-word without context-aware corrections"],"requires":["Speech-to-text API with sufficient quota (likely 100+ hours/month for free tier)","Audio quality minimum 16kHz sample rate, mono or stereo","Internet connectivity for cloud-based STT services"],"input_types":["audio stream (WAV, MP3, or browser-native format)","optional language specification (English, Spanish, Mandarin, etc.)"],"output_types":["timestamped transcript text","confidence scores per word (if STT engine provides)","speaker labels (user vs. background)","exportable transcript formats (TXT, JSON, SRT)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_verbaly__cap_3","uri":"capability://text.generation.language.personalized.feedback.generation.with.actionable.recommendations","name":"personalized feedback generation with actionable recommendations","description":"Synthesizes real-time metrics (speech rate, filler words, clarity) and conversation context into natural language feedback and specific, actionable recommendations. Uses rule-based logic and/or LLM-based generation to translate raw metrics into coaching advice (e.g., 'You used 12 filler words in 3 minutes — try pausing instead of saying um' or 'Your pace was 180 WPM, which is 20% faster than recommended for presentations — slow down by 10-15%'). Feedback is delivered immediately after speech or at session end.","intents":["I want specific, actionable advice on how to improve my speaking, not just metrics","I need to understand why my speech rate is problematic and what target I should aim for","I want recommendations tailored to my specific weak areas (filler words vs. pace vs. clarity)","I need encouragement and progress validation, not just criticism"],"best_for":["users who respond better to coaching-style guidance than raw metrics","professionals with limited time who need quick, implementable improvements","learners who benefit from positive reinforcement alongside constructive feedback"],"limitations":["Feedback quality depends on accuracy of underlying metrics — garbage metrics produce garbage recommendations","Generic recommendations may not account for context (e.g., slower pace is appropriate for technical presentations but not for motivational speeches)","No long-term learning — recommendations don't adapt based on user's historical progress or demonstrated improvements","LLM-based feedback generation can produce inconsistent tone or occasionally irrelevant suggestions"],"requires":["Accurate speech metrics from real-time analysis engine","LLM API (if using generative feedback) or rule-based feedback template engine","User context (presentation type, audience, goals) for contextual recommendations"],"input_types":["structured metrics object: {speechRate, fillerWordCount, clarityScore, pausePatterns}","conversation transcript and context","user profile or session goals (optional)"],"output_types":["natural language feedback summary (2-5 sentences)","prioritized list of 2-3 specific recommendations","target metrics (e.g., 'aim for 120-150 WPM for this presentation type')","encouragement or progress validation message"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_verbaly__cap_4","uri":"capability://memory.knowledge.session.recording.and.playback.with.synchronized.metrics.overlay","name":"session recording and playback with synchronized metrics overlay","description":"Records user audio during practice sessions and stores it with associated metadata (metrics, timestamps, transcript). Enables playback of the recording with real-time metric visualization overlaid on the timeline (e.g., visual indicators of filler words, pace changes, clarity dips at specific timestamps). Users can scrub through the recording, see exactly when they used a filler word or spoke too fast, and correlate audio with metrics for self-directed learning.","intents":["I want to listen back to my speech and see exactly where I made mistakes","I need to visualize my pace and filler word patterns across the entire session","I want to compare my performance across multiple practice sessions to track improvement","I need to share my practice recording with a human coach for additional feedback"],"best_for":["visual learners who benefit from seeing metrics synchronized with audio","users who want to self-review and identify patterns in their speech","professionals preparing for high-stakes presentations who want detailed post-session analysis"],"limitations":["Storage requirements scale with session duration — 1 hour of audio + metadata ≈ 50-100 MB, limiting free tier capacity","Playback UI complexity increases with metric density — dense filler word usage or rapid pace changes can clutter the timeline visualization","No automatic highlight generation — users must manually scrub to find specific issues rather than jumping to problem areas","Sharing recordings requires explicit user consent and secure storage to protect privacy"],"requires":["Audio storage backend (cloud storage like AWS S3, Google Cloud Storage, or on-device)","Metadata database to store metrics, timestamps, and transcript associations","Frontend UI capable of synchronized audio playback and timeline visualization (likely HTML5 audio + Canvas or WebGL)","Sufficient storage quota (likely 100-500 MB per free user)"],"input_types":["audio stream from practice session","metrics data with timestamps","transcript with word-level timestamps"],"output_types":["playable audio file (MP3, WAV, or browser-native format)","interactive timeline visualization with metric overlays","exportable session report (PDF or JSON)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_verbaly__cap_5","uri":"capability://memory.knowledge.progress.tracking.and.historical.session.comparison","name":"progress tracking and historical session comparison","description":"Maintains a persistent record of user practice sessions over time, storing metrics, transcripts, and feedback for each session. Enables users to view trends (e.g., 'Your average filler word count has decreased from 15 to 8 over the last 10 sessions') and compare specific metrics across sessions to visualize improvement. Likely uses a user database with session indexing and basic analytics (average, trend, percentile) to surface progress without requiring manual analysis.","intents":["I want to see if I'm actually improving over time or just practicing without progress","I need to identify which areas have improved and which still need work","I want to compare my performance on similar scenarios (e.g., interview practice) across multiple sessions","I need motivation through visible progress metrics to sustain long-term practice"],"best_for":["users committed to long-term speech improvement who need progress validation","professionals tracking improvement toward specific goals (e.g., reducing filler words by 50%)","learners who benefit from gamification and progress visualization for habit formation"],"limitations":["Requires persistent user accounts and session storage — free tier likely limits historical data retention (e.g., last 30 days or 10 sessions)","Trend analysis is statistical only — cannot identify root causes of improvement or regression","No predictive analytics — cannot forecast when user will reach specific goals","Privacy concerns with storing audio recordings and transcripts long-term"],"requires":["User authentication system (email, OAuth, or SSO)","Session database with indexed queries for historical retrieval","Analytics engine for computing trends, averages, and comparisons","Data retention policy and privacy compliance (GDPR, CCPA)"],"input_types":["session metrics from multiple practice sessions","user-defined goals or target metrics (optional)"],"output_types":["progress dashboard with trend charts (line graphs, bar charts)","session comparison table (side-by-side metrics)","milestone notifications (e.g., 'You've reduced filler words by 50%!')","exportable progress report (PDF)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_verbaly__cap_6","uri":"capability://planning.reasoning.scenario.based.practice.templates.with.context.customization","name":"scenario-based practice templates with context customization","description":"Provides pre-built practice scenarios (job interview, sales pitch, presentation, negotiation, etc.) that configure the AI conversation partner's role, expected questions, and difficulty level. Users select a scenario, optionally customize context (industry, role, audience type), and the system initializes the AI with appropriate prompts and constraints. This reduces setup friction and ensures users practice realistic, relevant conversations rather than generic dialogue.","intents":["I want to practice a job interview without having to describe the role and company to the AI","I need to practice a sales pitch with realistic objections from a skeptical buyer","I want to simulate a board presentation where the AI asks tough questions about my strategy","I need to practice difficult conversations (performance feedback, salary negotiation) with realistic scenarios"],"best_for":["users who benefit from structured practice with realistic scenarios","professionals preparing for specific, high-stakes conversations","learners who want guided practice rather than open-ended dialogue"],"limitations":["Pre-built scenarios are generic and may not match user's specific context (e.g., 'tech startup pitch' doesn't account for user's specific product)","Limited customization depth — users likely cannot fully define conversation flow or expected questions","Scenario library is fixed — no user-generated scenarios or community contributions","Difficulty levels are coarse-grained (easy/medium/hard) rather than adaptive to user's actual skill level"],"requires":["Scenario template database with prompt engineering for each scenario type","LLM with sufficient context window to maintain scenario constraints throughout conversation","Optional: user input for context customization (industry, role, audience)"],"input_types":["scenario selection (dropdown or search)","optional context parameters: {industry, role, audience, difficulty, duration}","user speech input during scenario execution"],"output_types":["initialized AI conversation partner with scenario-specific behavior","scenario-specific feedback (e.g., 'You addressed 3 of 5 common objections')","scenario completion summary"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_verbaly__cap_7","uri":"capability://tool.use.integration.browser.based.real.time.processing.without.server.dependency","name":"browser-based real-time processing without server dependency","description":"Implements core speech analysis (filler word detection, pace calculation, clarity metrics) using client-side JavaScript libraries and WebRTC audio processing, reducing latency and server load. While some features (LLM-based feedback, STT) likely require cloud APIs, the real-time metric computation happens in-browser, enabling low-latency feedback even with network delays. This architecture choice prioritizes responsiveness and user privacy (audio processing happens locally before transmission).","intents":["I want real-time feedback without noticeable lag that would disrupt my practice","I want my audio to be processed locally as much as possible for privacy","I want the tool to work smoothly even with variable network conditions","I want to practice offline for basic metrics without requiring constant cloud connectivity"],"best_for":["users concerned about audio privacy and local processing","users with variable or limited internet connectivity","developers building speech analysis features who want to minimize cloud API costs"],"limitations":["Client-side processing is limited to simple signal processing — advanced ML models (speaker diarization, emotion detection) still require cloud APIs","Browser compatibility varies — older browsers or mobile browsers may lack WebRTC support or have performance limitations","JavaScript performance is slower than native code — complex audio processing may introduce latency on lower-end devices","No persistent state — client-side processing cannot access historical data or user models without server synchronization"],"requires":["Modern web browser with WebRTC support (Chrome 25+, Firefox 22+, Safari 11+, Edge 79+)","JavaScript runtime with sufficient performance (typically 2+ GHz CPU)","Microphone access with browser permissions","Optional: cloud APIs for STT, LLM, and advanced analysis"],"input_types":["audio stream from WebRTC getUserMedia API","user-defined analysis parameters (filler word list, pace thresholds)"],"output_types":["real-time metric updates (speech rate, filler word count, clarity score)","visual feedback (progress bars, metric displays)","optional: cloud-based feedback (LLM recommendations, STT transcript)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Modern web browser with WebRTC support (Chrome 25+, Firefox 22+, Safari 11+)","Microphone access with at least 16kHz sample rate","Stable internet connection for streaming audio to backend analysis service","JavaScript enabled for real-time UI updates","Speech-to-text API (likely Google Cloud Speech-to-Text, Azure Speech Services, or Whisper)","LLM API access (OpenAI GPT, Anthropic Claude, or proprietary model)","Text-to-speech synthesis for AI responses (optional but likely included for full conversational experience)","Minimum 2-3 second latency tolerance for AI response generation","Speech-to-text API with sufficient quota (likely 100+ hours/month for free tier)","Audio quality minimum 16kHz sample rate, mono or stereo"],"failure_modes":["Accuracy of filler word detection depends on audio quality and background noise — may produce false positives in noisy environments","Real-time processing latency (likely 500ms-2s) means feedback is slightly delayed, not truly instantaneous","Metrics are acoustic-only — cannot assess content quality, logical flow, or persuasiveness of speech","No speaker diarization — cannot distinguish between user speech and background voices in multi-speaker scenarios","AI responses, while contextual, lack true understanding of nuanced body language, tone interpretation, and emotional subtext that human evaluators would provide","Speech-to-text transcription errors can cause the AI to misunderstand user intent and generate off-topic responses, breaking conversation flow","Limited scenario customization — likely offers pre-built templates rather than fully user-defined conversation contexts","No memory across sessions — each practice starts fresh without learning user's specific weak areas","Transcription accuracy varies with audio quality, accent, and background noise — typically 85-95% accuracy depending on STT engine","Real-time transcription introduces 1-3 second latency before text appears, potentially disrupting user focus during active speech","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.116Z","last_scraped_at":"2026-04-05T13:23:42.559Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=verbaly","compare_url":"https://unfragile.ai/compare?artifact=verbaly"}},"signature":"FMPE9b6H20j2yxtwuRSy/1eVQ2XsB3Z/AcQnCgq98Q6OnLI2LEueyEiZ/g1PjtJLH/Fgjmg++nWIRT307tHmBQ==","signedAt":"2026-06-22T02:39:22.511Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/verbaly","artifact":"https://unfragile.ai/verbaly","verify":"https://unfragile.ai/api/v1/verify?slug=verbaly","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}