Open AccessEvaluation of Automatic Speech Recognition Systems
Author(s) -
Matheus Xavier Sampaio,
Régis Pires Magalhães,
Ticiana Linhares Coelho da Silva,
Lívia Almada Cruz,
Davi Romero de Vasconcelos,
José Antônio Fernandes de Macêdo,
Marianna Gonçalves Fontenele Ferreira
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/sbbd.2021.17889
Subject(s) - computer science , speech recognition , task (project management) , speech technology , speech analytics , cloud computing , deep learning , popularity , voice activity detection , natural language processing , artificial intelligence , speaker recognition , speech processing , engineering , operating system , psychology , social psychology , systems engineering
Automatic Speech Recognition (ASR) is an essential task for many applications like automatic caption generation for videos, voice search, voice commands for smart homes, and chatbots. Due to the increasing popularity of these applications and the advances in deep learning models for transcribing speech into text, this work aims to evaluate the performance of commercial solutions for ASR that use deep learning models, such as Facebook Wit.ai, Microsoft Azure Speech, and Google Cloud Speech-to-Text. The results demonstrate that the evaluated solutions slightly differ. However, Microsoft Azure Speech outperformed the other analyzed APIs.