Speech Recognition platform
Our speech-to-text solution offers high-quality language processing that is independent of accent and channel type. It features technologies for speaker identification and voice activity detection, and is resilient to moderate background noise. The end-to-end pipeline for training—from audio to text—has achieved several key design objectives, including a language-agnostic framework, a low word error rate, noise resistance, independence from telephone or broadcast sources, and universal accent modeling. We’ve tested the platform for English language modeling, and results can be experienced on a demo platform. The automatic use of modern GPUs enables efficient processing of large speech volumes, making the solution suitable for tight budgets and heavy speech tasks. Additionally, the platform incorporates an automatic speaker identification feature that segments the audio stream into segments with different speakers, allowing for differentiation among speakers, recognition of known individuals, or as a secondary method for validating identities in another service. Another feature we've developed is a configurable voice activity detector, capable of distinguishing between speech, music, noise, and other types of data within the voice stream, which can also be trained as an independent solution.