Speech Recognition & Transcription
Convert speech to text using ASR systems — from traditional acoustic models to modern end-to-end deep learning approaches. Speech Recognition Basics Automatic Speech Recognition (ASR) converts audio signals into text. The pipeline typically includes acoustic processing (feature extraction), acoustic modeling (mapping sound to phonemes), language modeling (predicting word sequences), and decoding (finding the most likely transcription). Modern end-to-end systems combine these into a single neural network. Modern ASR Systems DeepSpeech (Mozilla) uses a recurrent neural network trained on thousands of hours of speech. Whisper (OpenAI) is a transformer-based system supporting 100+ languages with robust punctuation and formatting. Wav2Vec 2.0 (Meta) learns speech representations from unlabeled audio, requiring minimal labeled data for fine-tuning. These systems handle background noise, multiple speakers, and various accents. Real-Time vs Batch Processing Real-time ASR processes audio as it arrives, providing live captions or voice commands. It requires streaming models with low latency (under 300ms). Batch ASR processes pre-recorded audio and can use larger, more accurate models. Web Speech API provides browser-based real-time ASR. For production, consider cloud APIs (Google, AWS, Azure) or self-hosted Whisper for privacy-sensitive applications. Accuracy & Language Support Word Error Rate (WER) measures accuracy — state-of-the-art models achieve 5-10% WER on clean speech. Accuracy degrades with background noise, accents, domain-specific vocabulary, and overlapping speech. Solutions: fine-tune on domain data, use language model biasing for specific terms, implement voice activity detection (VAD) to filter silence. Most modern systems support 50-100+ languages with varying accuracy.