{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_speakfit-club","slug":"speakfit-club","name":"SpeakFit.club","type":"webapp","url":"https://www.speakfit.club","page_url":"https://unfragile.ai/speakfit-club","categories":["research-search"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_speakfit-club__cap_0","uri":"capability://data.processing.analysis.real.time.speech.recognition.and.transcription.across.multiple.languages","name":"real-time speech recognition and transcription across multiple languages","description":"Captures audio input from user microphone, processes it through a multilingual speech-to-text engine (likely cloud-based ASR via third-party provider like Google Cloud Speech-to-Text or Azure Speech Services), and converts spoken utterances into text transcripts. The system maintains language context to optimize recognition accuracy for the target language being practiced, with fallback mechanisms for lower-confidence segments.","intents":["I need to record my spoken practice and see what I actually said","I want the system to understand my accent and transcribe my speech accurately","I'm practicing multiple languages and need language-aware transcription"],"best_for":["Non-native speakers practicing major languages (English, Spanish, French, Mandarin)","Professionals preparing for multilingual interviews or presentations","Language learners seeking immediate feedback on pronunciation"],"limitations":["Speech recognition accuracy degrades significantly for low-resource languages and heavy accents (typically 70-85% accuracy vs 95%+ for English)","Background noise and audio quality directly impact transcription reliability","Real-time processing introduces 500ms-2s latency depending on audio chunk size and provider","No speaker diarization — cannot distinguish between multiple speakers in a single recording"],"requires":["Browser with Web Audio API support (Chrome 25+, Firefox 25+, Safari 14.1+)","Microphone hardware with minimum 16kHz sampling rate","Active internet connection for cloud-based ASR","Target language specified before recording session"],"input_types":["audio/wav","audio/mp3","audio/webm","raw PCM audio stream from Web Audio API"],"output_types":["text transcript","confidence scores per word","timing metadata (word-level timestamps)"],"categories":["data-processing-analysis","speech-recognition"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_speakfit-club__cap_1","uri":"capability://data.processing.analysis.ai.powered.pronunciation.and.accent.feedback.generation","name":"ai-powered pronunciation and accent feedback generation","description":"Analyzes the transcribed speech against target pronunciation patterns using phonetic analysis and prosody detection. The system compares the user's audio waveform characteristics (pitch, stress patterns, vowel formants, consonant articulation) against native speaker reference models, then generates structured feedback identifying specific phonemes, stress patterns, or intonation issues. Uses deep learning models trained on multilingual speech corpora to detect deviation from native pronunciation norms.","intents":["I want to know which specific sounds I'm mispronouncing","I need feedback on my stress and intonation patterns","I want to compare my pronunciation to a native speaker"],"best_for":["Non-native speakers with intermediate+ language proficiency seeking accent reduction","Professionals preparing for high-stakes presentations or interviews","Learners of tonal languages (Mandarin, Vietnamese) needing pitch feedback"],"limitations":["Phonetic analysis accuracy varies by language pair — most reliable for English, less reliable for tonal languages and languages with complex consonant clusters","Cannot provide feedback on pragmatic or discourse-level issues (only phonetic level)","Requires high-quality audio input (SNR >20dB) for accurate formant analysis","Does not account for regional accent variations within native speakers"],"requires":["Transcribed text from speech recognition capability","Original audio waveform with metadata (sample rate, duration)","Target language and native accent variant specified (e.g., 'American English' vs 'British English')"],"input_types":["audio waveform (PCM or compressed)","text transcript with word-level timestamps","target language code (ISO 639-1)"],"output_types":["structured feedback JSON with phoneme-level corrections","prosody analysis (pitch contour, stress patterns)","visual feedback (spectrogram overlays, formant charts)","severity scores (1-5 scale per issue)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_speakfit-club__cap_2","uri":"capability://planning.reasoning.personalized.speaking.practice.session.generation.and.sequencing","name":"personalized speaking practice session generation and sequencing","description":"Generates contextually-relevant speaking prompts and exercises tailored to the user's proficiency level, learning goals, and previous performance. Uses a rule-based or ML-based system to sequence exercises from easier to harder, track which topics/phonemes the user struggles with, and adaptively select next prompts to target weak areas. May integrate spaced repetition principles to resurface challenging content at optimal intervals.","intents":["I want practice exercises matched to my current level, not too easy or too hard","I want to focus on the specific areas where I'm struggling","I want a structured learning path that builds progressively"],"best_for":["Self-directed language learners without access to tutors","Professionals on tight timelines needing efficient, targeted practice","Learners who benefit from adaptive difficulty scaling"],"limitations":["Adaptive sequencing requires sufficient historical performance data — cold-start users get generic exercises","Cannot assess pragmatic appropriateness or cultural context of responses (only phonetic/grammatical)","Exercise generation quality depends on underlying prompt templates — may produce repetitive or contextually awkward scenarios","No human oversight of exercise relevance — may miss domain-specific vocabulary needs"],"requires":["User proficiency level assessment (CEFR A1-C2 or equivalent)","Learning goal specification (e.g., 'business English', 'casual conversation')","Historical performance data (minimum 3-5 prior sessions for effective personalization)","Target language and variant specified"],"input_types":["user proficiency level (categorical or numeric)","learning goal (text description or category)","previous session performance data (JSON with scores, timestamps, topics)"],"output_types":["speaking prompt (text or audio)","expected response template or key points","difficulty rating (1-10)","topic/skill tags","estimated time to complete"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_speakfit-club__cap_3","uri":"capability://text.generation.language.conversational.dialogue.simulation.with.ai.speaking.partner","name":"conversational dialogue simulation with ai speaking partner","description":"Provides an interactive conversational partner (likely powered by a large language model like GPT-4 or similar) that engages the user in realistic dialogue scenarios. The system generates contextually appropriate responses to user utterances, maintains conversation state across multiple turns, and can simulate different conversation contexts (job interview, casual chat, customer service, etc.). Speech input from the user is transcribed, processed by the LLM, and the LLM's text response is converted back to speech via text-to-speech synthesis.","intents":["I want to practice real conversations without a human partner","I want to simulate specific scenarios like job interviews or business meetings","I want feedback on my conversational fluency and naturalness"],"best_for":["Intermediate+ learners ready for conversational practice","Professionals preparing for specific interview or business scenarios","Learners who are self-conscious about speaking with humans"],"limitations":["LLM responses may not always be contextually appropriate or realistic — can produce awkward or unnatural dialogue","No true understanding of user intent — responds to transcribed text, not actual meaning, so misrecognitions compound errors","Text-to-speech synthesis may sound robotic or unnatural, reducing immersion","Cannot assess non-verbal communication (body language, eye contact) which is critical for real interviews","LLM may be overly forgiving or not provide corrective feedback on errors"],"requires":["Speech recognition capability (for transcribing user input)","Text-to-speech synthesis engine (for generating partner responses)","LLM API access (OpenAI, Anthropic, or self-hosted model)","Conversation scenario templates or context specification"],"input_types":["audio (user speech)","scenario context (text description or category)","conversation history (previous turns)"],"output_types":["text response from AI partner","audio synthesis of response","conversation transcript","optional: feedback on user's conversational quality"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_speakfit-club__cap_4","uri":"capability://data.processing.analysis.performance.tracking.and.progress.analytics.dashboard","name":"performance tracking and progress analytics dashboard","description":"Aggregates user session data (transcripts, pronunciation scores, exercise completion, dialogue quality metrics) into a persistent user profile and generates visualizations of progress over time. Tracks metrics like accuracy improvement, vocabulary growth, phoneme mastery, and conversation fluency. Provides comparative analytics (e.g., 'your /r/ pronunciation improved 15% this week') and identifies trends to highlight areas of consistent improvement or stagnation.","intents":["I want to see if I'm actually improving over time","I want to identify which specific skills are getting better or worse","I want motivation through visible progress metrics"],"best_for":["Self-motivated learners who respond well to quantified progress","Professionals tracking improvement toward specific milestones","Learners who need data to justify continued platform use"],"limitations":["Metrics are only as good as underlying assessment — garbage in, garbage out if speech recognition or feedback generation is inaccurate","Cannot measure real-world speaking ability — only platform-specific metrics","Requires sufficient historical data (minimum 10-20 sessions) for meaningful trend analysis","May create false sense of progress if metrics are inflated or not aligned with actual proficiency"],"requires":["User account with persistent storage","Minimum 3-5 completed practice sessions","Consistent metric collection across all sessions"],"input_types":["session performance data (JSON with scores, timestamps, exercise metadata)","user proficiency assessments","exercise completion records"],"output_types":["progress charts (line graphs, bar charts)","skill breakdown (phoneme mastery, grammar accuracy, vocabulary)","comparative metrics (week-over-week, month-over-month)","milestone achievements (badges, certificates)","recommendations for focus areas"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_speakfit-club__cap_5","uri":"capability://text.generation.language.multilingual.language.model.based.response.evaluation.and.scoring","name":"multilingual language model-based response evaluation and scoring","description":"Uses a fine-tuned or prompt-engineered language model to evaluate the quality of user responses in dialogue scenarios or open-ended speaking exercises. The model assesses multiple dimensions: grammatical correctness, vocabulary appropriateness, fluency, coherence, and relevance to the prompt. Generates scores (numeric or categorical) and natural language feedback explaining strengths and areas for improvement. May use rubric-based evaluation (predefined criteria) or open-ended LLM assessment.","intents":["I want feedback on whether my response was grammatically correct and appropriate","I want to know if I answered the question fully and coherently","I want suggestions for how to improve my response"],"best_for":["Learners seeking comprehensive feedback beyond just pronunciation","Advanced learners (B2+) focused on nuance and appropriateness","Professionals needing feedback on business communication quality"],"limitations":["LLM evaluation can be inconsistent or biased toward certain response styles","Cannot assess tone, politeness, or cultural appropriateness with high reliability","Requires accurate transcription — errors in transcription lead to unfair evaluation","May penalize creative or non-standard responses that are actually appropriate","Evaluation quality depends heavily on prompt engineering and model capability"],"requires":["Transcribed user response (text)","Original prompt or scenario context","Target language and proficiency level","Evaluation rubric or criteria (optional but improves consistency)"],"input_types":["user response transcript (text)","prompt or scenario context (text)","target language (code)","proficiency level (categorical)"],"output_types":["numeric scores (1-10 or 0-100 scale) per dimension","categorical ratings (Excellent/Good/Fair/Poor)","natural language feedback (text explanation)","specific suggestions for improvement","comparison to expected response (if available)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_speakfit-club__cap_6","uri":"capability://text.generation.language.text.to.speech.synthesis.for.dialogue.partner.responses.and.pronunciation.models","name":"text-to-speech synthesis for dialogue partner responses and pronunciation models","description":"Converts text responses from the AI dialogue partner and pronunciation reference models into natural-sounding speech audio. Uses a neural text-to-speech engine (likely cloud-based like Google Cloud Text-to-Speech, Azure Speech Synthesis, or similar) with support for multiple languages and voice variants. May include prosody control to emphasize stress patterns or intonation for teaching purposes. Generates audio in real-time or near-real-time for conversational responsiveness.","intents":["I want to hear how native speakers pronounce words and phrases","I want the AI dialogue partner to speak naturally so I can practice listening comprehension","I want to hear different accent variants for comparison"],"best_for":["Learners who benefit from auditory input and modeling","Conversational practice scenarios requiring realistic dialogue","Pronunciation learners needing reference models"],"limitations":["Neural TTS quality varies by language — excellent for English, degraded for low-resource languages","Synthesized speech may sound robotic or unnatural, reducing immersion and realism","Cannot capture all prosodic nuances of natural speech (emotion, hesitation, etc.)","Real-time synthesis adds latency (typically 1-3 seconds) to conversational flow","Limited voice variety — may not offer all accent variants or speaker demographics"],"requires":["Text input (response or pronunciation model)","Target language and voice variant specified","Internet connection for cloud-based TTS","Audio output capability (speakers or headphones)"],"input_types":["text (response or phrase to synthesize)","language code (ISO 639-1)","voice variant (e.g., 'en-US-Neural2-A', 'es-ES-Neural2-B')","optional: prosody markup (SSML for emphasis, rate, pitch control)"],"output_types":["audio/mp3 or audio/wav","timing metadata (phoneme-level timestamps)","prosody information (pitch contour, stress patterns)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_speakfit-club__cap_7","uri":"capability://data.processing.analysis.user.proficiency.assessment.and.level.classification","name":"user proficiency assessment and level classification","description":"Evaluates user language proficiency through initial diagnostic tests or ongoing performance monitoring and assigns a proficiency level (typically CEFR A1-C2 or equivalent numeric scale). May use a combination of approaches: initial placement test with multiple-choice or speaking tasks, adaptive testing that adjusts difficulty based on responses, or inference from historical performance data. Classifies users into proficiency bands to enable appropriate exercise sequencing and feedback calibration.","intents":["I want to know my current language level so I can find appropriate exercises","I want to track how my proficiency level changes over time","I want the system to adjust difficulty based on my actual ability"],"best_for":["New users establishing a baseline for personalization","Learners who want standardized proficiency assessment","Platforms needing to segment users for appropriate content"],"limitations":["Initial assessment may be inaccurate if user is nervous or unfamiliar with testing format","CEFR levels are broad bands — two users at 'B1' may have very different strengths/weaknesses","Assessment is limited to speaking skills — doesn't measure reading, writing, or listening","Requires sufficient test items or performance data for reliable classification","May not capture recent improvement if assessment is infrequent"],"requires":["Initial diagnostic test or historical performance data","Proficiency scale definition (CEFR, numeric, or custom)","Test items or performance rubrics for classification"],"input_types":["diagnostic test responses (audio or text)","historical session performance data (JSON)","user self-assessment (optional)"],"output_types":["proficiency level (CEFR A1-C2 or numeric 1-10)","confidence score (0-100%)","skill breakdown (phoneme accuracy, grammar, vocabulary, fluency)","recommendations for next level"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_speakfit-club__cap_8","uri":"capability://memory.knowledge.user.account.management.and.session.persistence","name":"user account management and session persistence","description":"Manages user authentication, account creation, and persistent storage of user profiles, session history, and progress data. Stores encrypted credentials, maintains session tokens for web access, and persists all user data (transcripts, scores, preferences) in a backend database. Enables users to resume practice sessions, access historical data, and maintain continuity across multiple devices or sessions.","intents":["I want to create an account and log in securely","I want my progress saved so I can pick up where I left off","I want to access my data from different devices"],"best_for":["Any user of the platform requiring account-based personalization","Users practicing across multiple devices or sessions","Platforms needing to track long-term progress"],"limitations":["Requires backend infrastructure and database maintenance","Data privacy and security are critical — any breach exposes user data and audio recordings","Cross-device sync may have latency or consistency issues","Freemium model may limit storage or data retention for free users"],"requires":["Backend server with database (SQL or NoSQL)","Authentication mechanism (OAuth, email/password, SSO)","Secure credential storage (hashed passwords, encrypted tokens)","HTTPS/TLS for secure data transmission"],"input_types":["user credentials (email, password, or OAuth token)","session data (performance metrics, transcripts, preferences)"],"output_types":["session token (JWT or similar)","user profile (metadata, preferences, proficiency level)","historical data (session logs, progress metrics)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Browser with Web Audio API support (Chrome 25+, Firefox 25+, Safari 14.1+)","Microphone hardware with minimum 16kHz sampling rate","Active internet connection for cloud-based ASR","Target language specified before recording session","Transcribed text from speech recognition capability","Original audio waveform with metadata (sample rate, duration)","Target language and native accent variant specified (e.g., 'American English' vs 'British English')","User proficiency level assessment (CEFR A1-C2 or equivalent)","Learning goal specification (e.g., 'business English', 'casual conversation')","Historical performance data (minimum 3-5 prior sessions for effective personalization)"],"failure_modes":["Speech recognition accuracy degrades significantly for low-resource languages and heavy accents (typically 70-85% accuracy vs 95%+ for English)","Background noise and audio quality directly impact transcription reliability","Real-time processing introduces 500ms-2s latency depending on audio chunk size and provider","No speaker diarization — cannot distinguish between multiple speakers in a single recording","Phonetic analysis accuracy varies by language pair — most reliable for English, less reliable for tonal languages and languages with complex consonant clusters","Cannot provide feedback on pragmatic or discourse-level issues (only phonetic level)","Requires high-quality audio input (SNR >20dB) for accurate formant analysis","Does not account for regional accent variations within native speakers","Adaptive sequencing requires sufficient historical performance data — cold-start users get generic exercises","Cannot assess pragmatic appropriateness or cultural context of responses (only phonetic/grammatical)","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":"2026-05-24T12:16:33.096Z","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=speakfit-club","compare_url":"https://unfragile.ai/compare?artifact=speakfit-club"}},"signature":"XFt0Bs01wuj6HJIO9/jDns5tRqlbnkUflb92wQOMZm3WelQi1MmJAQi3ApXtRbVwiiYQxqcs2NeNlAU4d8MDDw==","signedAt":"2026-06-21T20:17:19.803Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/speakfit-club","artifact":"https://unfragile.ai/speakfit-club","verify":"https://unfragile.ai/api/v1/verify?slug=speakfit-club","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"}}