Speechllect

Converts live audio input into text using an underlying speech recognition engine (likely cloud-based ASR via Web Audio API or similar browser-native APIs). The system captures audio streams in real-time, processes them through a speech recognition model, and returns transcribed text with minimal latency. Architecture appears to be browser-first with client-side audio capture, suggesting either local processing or low-latency cloud inference.

Analyzes audio input or transcribed text to detect and classify emotional states (e.g., happy, sad, angry, neutral, frustrated) and returns sentiment labels alongside transcription. The implementation likely uses either acoustic feature extraction from raw audio (pitch, tone, speech rate) or NLP-based sentiment classification on transcribed text, or a hybrid approach. Sentiment labels are surfaced in real-time or near-real-time during or immediately after transcription.

Offers a free tier of the product accessible without payment information or account verification, allowing users to test core transcription and emotion detection features before committing to paid plans. The freemium model likely includes usage limits (e.g., minutes per month, number of sessions) and may restrict advanced features to paid tiers. No credit card requirement lowers friction for initial adoption.

Provides a simplified, focused UI optimized for voice input with minimal menu complexity or feature discovery overhead. The interface likely centers on a single 'record' button or similar primary action, with emotion and transcription results displayed inline or in a sidebar. Design prioritizes ease-of-use for non-technical users (therapists, coaches) over feature richness, reducing cognitive load during active listening.

Organizes transcriptions and emotion data into discrete sessions (e.g., therapy sessions, customer calls) with metadata (timestamp, duration, participants). Sessions are stored and retrievable for later review, comparison, or export. Architecture likely uses a simple database (SQL or NoSQL) to persist session records with associated transcripts and emotion labels, indexed by user and timestamp for retrieval.