Support software for Automatic Speech Recognition systems targeted for non-native speech

2020 ◽  
Author(s):  
Kacper Radzikowski ◽  
Osamu Yoshie ◽  
Robert Nowak
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Native Speech ◽  
Recognition Systems ◽  
Support Software

scholarly journals Evaluation of Automatic Speech Recognition Systems

2021 ◽  
Author(s):  
Matheus Xavier Sampaio ◽  
Regis Pires Magalhães ◽  
Ticiana Linhares Coelho da Silva ◽  
Lívia Almada Cruz ◽  
Davi Romero de Vasconcelos ◽  
...  
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Smart Homes ◽  
The Other ◽  
Learning Models ◽  
Recognition Systems ◽  
Microsoft Azure

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.

scholarly journals Improving Aphasic Speech Recognition by Using Novel Semi-Supervised Learning Methods on AphasiaBank for English and Spanish

2021 ◽  
Vol 11 (19) ◽  
pp. 8872
Author(s):  
Iván G. Torre ◽  
Mónica Romero ◽  
Aitor Álvarez
Keyword(s):  
Speech Recognition ◽  
Supervised Learning ◽  
Automatic Speech Recognition ◽  
English Language ◽  
Spanish Language ◽  
Learning Methods ◽  
Text Data ◽  
Lower Performance ◽  
Recognition Systems ◽  
Fine Tune

Automatic speech recognition in patients with aphasia is a challenging task for which studies have been published in a few languages. Reasonably, the systems reported in the literature within this field show significantly lower performance than those focused on transcribing non-pathological clean speech. It is mainly due to the difficulty of recognizing a more unintelligible voice, as well as due to the scarcity of annotated aphasic data. This work is mainly focused on applying novel semi-supervised learning methods to the AphasiaBank dataset in order to deal with these two major issues, reporting improvements for the English language and providing the first benchmark for the Spanish language for which less than one hour of transcribed aphasic speech was used for training. In addition, the influence of reinforcing the training and decoding processes with out-of-domain acoustic and text data is described by using different strategies and configurations to fine-tune the hyperparameters and the final recognition systems. The interesting results obtained encourage extending this technological approach to other languages and scenarios where the scarcity of annotated data to train recognition models is a challenging reality.

On the Application of Automated Software Testing Techniques to the Development and Maintenance of Speech Recognition Systems

2012 ◽  
pp. 30-48
Author(s):  
Daniel Bolanos
Keyword(s):  
Speech Recognition ◽  
Software Testing ◽  
Automatic Speech Recognition ◽  
Automated Testing ◽  
Automated Software Testing ◽  
Testing Framework ◽  
Methods And Techniques ◽  
Testing Techniques ◽  
Recognition Systems ◽  
Automated Software

This chapter provides practitioners in the field with a set of guidelines to help them through the process of elaborating an adequate automated testing framework to competently test automatic speech recognition systems. Through this chapter the testing process of such a system is analyzed from different angles, and different methods and techniques are proposed that are well suited for this task.

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