Real-time speech recognition in meetings converts spoken words into text instantaneously using a combination of audio processing, machine learning models, and streaming infrastructure. The process starts with capturing audio input from microphones, which is then processed to filter background noise and enhance speech clarity. The cleaned audio is split into small chunks (e.g., 100ms segments) and fed into a speech recognition model. This model typically uses an acoustic component to map audio features to phonemes (speech sounds) and a language component to predict the most likely words or phrases based on context. The system streams these predictions as they’re generated, allowing text to appear in near-real time. For example, cloud-based services like Google’s Speech-to-Text or open-source tools like Mozilla DeepSpeech handle this by processing audio incrementally while maintaining low latency.
The technical backbone involves three core components: acoustic models, language models, and streaming architecture. Acoustic models, often built using neural networks like convolutional neural networks (CNNs) or transformers, analyze the audio’s frequency patterns to identify phonemes. Language models, such as recurrent neural networks (RNNs) or transformer-based architectures, predict word sequences by leveraging context from previous words. Streaming is enabled through frameworks like WebSocket or gRPC, which allow continuous data transmission between clients and servers. For instance, a meeting app might use WebSocket to send audio chunks to a server, which processes them using a pre-trained model and immediately returns partial transcripts. Edge devices can also run lightweight models (e.g., TensorFlow Lite) to reduce latency further, bypassing cloud dependencies.
Challenges include handling overlapping speech, varying accents, and maintaining accuracy under low-latency constraints. To address overlapping voices, some systems use beamforming microphones or separate audio streams for each participant. Noise suppression algorithms, like spectral subtraction or deep learning-based tools (e.g., RNNoise), clean audio before processing. For accents, models are trained on diverse datasets containing multilingual or regional speech samples. Latency is minimized by optimizing model inference—using techniques like quantization (reducing numerical precision of model weights) or pruning (removing redundant neurons). Speaker diarization (identifying “who said what”) adds another layer, often using clustering algorithms like k-means on voice embeddings. For example, a meeting tool might combine real-time transcription with speaker labels by analyzing voice characteristics in parallel with speech recognition.