SpeakFit.club

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.

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.

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.

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.

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.

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.

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.

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.

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.