Automatic speech recognition datasets for Gronings, Nasal, and Besemah for experiments reported in Bartelds, San, McDonnell, Jurafsky and Wieling (2023). Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation. ACL 2023. Model training code available at: https://github.com/Bartelds/asr-augmentatio
Funding Information: This work was supported by the Academy of Finland (grants 329267, 330139). Publ...
Contains fulltext : 236648.pdf (Publisher’s version ) (Open Access)Radboud Univers...
We investigate the performance of self-supervised pretraining frameworks on pathological speech data...
In the paper, we present a software pipeline for speech recognition to automate the creation of trai...
Most widely spoken languages have numerous dialects or accents which can vary in degree of mutual in...
Recently there has been interest in the approaches for train-ing speech recognition systems for lang...
Automatic Speech Recognition (ASR) models can aid field linguists by facilitating the creation of te...
Dataset supporting 'Auditory inspired machine learning techniques can improve speech intelligibi...
There is growing recognition of the importance of data-centric methods for building machine learning...
Data augmentation is a technique to generate new training data based on existing data. We evaluate t...
This paper describes AaltoASR's speech recognition system for the INTERSPEECH 2020 shared task on Au...
Speech recognition centers on the use of natural speech for human-computer interaction providing com...
International audienceAutomatic Speech Recognition systems use signal processing and machine learnin...
Automatic speech recognition (ASR) technology has matured over the past few decades and has made sig...
abstract: Many tasks that humans do from day to day are taken for granted in term of appreciating th...
Funding Information: This work was supported by the Academy of Finland (grants 329267, 330139). Publ...
Contains fulltext : 236648.pdf (Publisher’s version ) (Open Access)Radboud Univers...
We investigate the performance of self-supervised pretraining frameworks on pathological speech data...
In the paper, we present a software pipeline for speech recognition to automate the creation of trai...
Most widely spoken languages have numerous dialects or accents which can vary in degree of mutual in...
Recently there has been interest in the approaches for train-ing speech recognition systems for lang...
Automatic Speech Recognition (ASR) models can aid field linguists by facilitating the creation of te...
Dataset supporting 'Auditory inspired machine learning techniques can improve speech intelligibi...
There is growing recognition of the importance of data-centric methods for building machine learning...
Data augmentation is a technique to generate new training data based on existing data. We evaluate t...
This paper describes AaltoASR's speech recognition system for the INTERSPEECH 2020 shared task on Au...
Speech recognition centers on the use of natural speech for human-computer interaction providing com...
International audienceAutomatic Speech Recognition systems use signal processing and machine learnin...
Automatic speech recognition (ASR) technology has matured over the past few decades and has made sig...
abstract: Many tasks that humans do from day to day are taken for granted in term of appreciating th...
Funding Information: This work was supported by the Academy of Finland (grants 329267, 330139). Publ...
Contains fulltext : 236648.pdf (Publisher’s version ) (Open Access)Radboud Univers...
We investigate the performance of self-supervised pretraining frameworks on pathological speech data...
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