Automatic Speech Recognition (ASR)

Automatic Speech Recognition (ASR) is an artificial intelligence-based technology that converts the acoustic signal of human speech into text format using machine learning (ML) algorithms and deep neural networks (DNN).

Modern ASR systems use Transformer architectures, such as Whisper or Conformer, or RNN architectures, including LSTM/GRU. These models simultaneously capture acoustic model and language model dependencies, ensuring high accuracy and a low WER (Word Error Rate) even under challenging conditions, including background noise, reverberation, different accents, or high speech rates.

Architecture and processing pipeline

1. Audio preprocessing: signal normalization and noise filtering.
2. Feature Extraction: conversion of the audio waveform into spectrograms or coefficients, such as MFCC or Log-Mel filterbanks.
3. Decoding: generation of the most probable sequence of tokens or words using acoustic and language models, with CTC loss or transformer-based inference.
4. Post-processing: restoration of punctuation, capitalization or truecasing, and text formatting through ITN (Inverse Text Normalization).

Deployment and use cases

• Production use cases: audio and video transcription, automatic subtitling, integration with IVR and contact centers, voice assistants (Voice AI), conversation analytics (Speech Analytics), and multimodal LLM systems.
• Infrastructure architectures: the technology can be deployed in the cloud as a cloud-native or SaaS API service, or on-premises within isolated environments. It supports two operating modes: Real-time Streaming for low-latency stream processing and Batch Processing for large-scale processing of recorded datasets.
• Service integration: due to its high scalability, ASR is a critical component of security systems, including biometrics and monitoring, MedTech solutions such as medical record dictation, EdTech, and media platforms.