iSpeech

Converts written text into natural-sounding speech across 50+ languages and regional dialects using neural vocoding and prosody modeling. The system maintains language-specific phoneme inventories and applies context-aware intonation patterns to generate speech that preserves semantic emphasis and emotional tone. Supports both real-time streaming synthesis and batch processing for high-volume content generation.

Converts audio streams (real-time or batch) into text using deep learning acoustic models trained on domain-specific corpora. The system supports multiple audio codecs and sample rates, applies noise suppression preprocessing, and can be configured with language-specific language models to improve accuracy for technical terminology, proper nouns, and domain jargon. Outputs include confidence scores per word and optional speaker diarization.

Automatically detects the language spoken in audio by analyzing acoustic and linguistic features. Supports 50+ languages and can identify language switches within a single audio stream. Uses deep learning models trained on multilingual corpora to classify language with high accuracy even in noisy conditions. Returns language codes, confidence scores, and optionally language-specific processing recommendations (e.g., recommended ASR model for detected language).

Authenticates users by analyzing unique voice characteristics (pitch, formant frequencies, spectral patterns) extracted from short audio samples (5-10 seconds). Uses speaker embedding models trained on large voice datasets to create voiceprints that are compared against enrolled templates using cosine similarity or probabilistic scoring. Supports both text-dependent (user speaks specific phrase) and text-independent (any speech) verification modes with configurable false acceptance/rejection thresholds.

Analyzes acoustic features (prosody, spectral characteristics, voice quality) from audio to classify emotional state and sentiment polarity. Extracts features including pitch contour, energy envelope, formant frequencies, and voice quality metrics, then applies trained classifiers to detect emotions (happiness, sadness, anger, frustration, neutral) and sentiment (positive, negative, neutral). Returns emotion scores and confidence levels per utterance or over sliding time windows for real-time analysis.

Identifies speech segments within audio streams using machine learning models trained to distinguish voice from background noise, silence, and non-speech sounds. Applies frame-level classification (typically 10-20ms frames) with smoothing to reduce false positives, then outputs voice activity boundaries with configurable sensitivity. Can automatically trim leading/trailing silence, remove background noise segments, or segment audio into speech/non-speech regions for downstream processing.

Creates synthetic voices from short audio samples (30 seconds to 5 minutes) of a target speaker by extracting speaker embeddings and fine-tuning neural vocoder parameters. Uses speaker adaptation techniques to transfer the unique voice characteristics (timbre, pitch range, speaking style) to a text-to-speech synthesis engine. Supports both real-time synthesis with cloned voices and batch processing for content generation, with optional style transfer for emotional expression.

Enables bidirectional voice conversations by orchestrating speech-to-text, language understanding, dialogue state management, and text-to-speech synthesis in a low-latency pipeline. Manages conversation context, turn-taking, and interruption handling through WebSocket or gRPC connections. Integrates with external NLU/dialogue systems (via API callbacks) or uses built-in intent classification for simple dialogue flows. Supports barge-in (user interruption), confirmation prompts, and error recovery.

Converts audio between multiple formats (WAV, MP3, OGG, FLAC, OPUS, AAC) and optimizes codec parameters (bitrate, sample rate, channels) for specific use cases. Supports batch processing of large audio libraries with configurable quality/compression tradeoffs. Applies format-specific optimizations (e.g., OPUS for low-bandwidth streaming, FLAC for lossless archival) and can normalize audio levels and sample rates across files.

Analyzes audio files to measure quality metrics (SNR, THD, frequency response, dynamic range) and identifies issues (noise, clipping, distortion). Applies enhancement algorithms including noise suppression, echo cancellation, automatic gain control (AGC), and equalization to improve audio quality. Supports both real-time enhancement for streaming and batch processing for archival. Returns quality scores before/after enhancement for validation.

Identifies speakers from audio by comparing speaker embeddings against an enrolled speaker database. Supports speaker enrollment (creating speaker profiles from audio samples), speaker identification (determining which enrolled speaker is speaking), and open-set identification (detecting unknown speakers). Uses deep learning models to extract speaker embeddings robust to content, language, and channel variations. Manages speaker database with APIs for enrollment, deletion, and profile updates.