TITLE: Impact of Dataset on Acoustic Models for Automatic Speech Recognition 

ABSTRACT: In Automatic Speech Recognition, GMM-HMM had been widely used for acoustic
modelling. With the current advancement of deep learning, the Gaussian Mixture
Model (GMM) from acoustic models has been replaced with Deep Neural Network,
namely DNN-HMM Acoustic Models. The GMM models are widely used to create the
alignments of the training data for the hybrid deep neural network model, thus
making it an important task to create accurate alignments. Many factors such as
training dataset size, training data augmentation, model hyperparameters, etc.,
affect the model learning. Traditionally in machine learning, larger datasets
tend to have better performance, while smaller datasets tend to trigger
over-fitting. The collection of speech data and their accurate transcriptions
is a significant challenge that varies over different languages, and in most
cases, it might be limited to big organizations. Moreover, in the case of
available large datasets, training a model using such data requires additional
time and computing resources, which may not be available. While the data about
the accuracy of state-of-the-art ASR models on open-source datasets are
published, the study about the impact of the size of a dataset on acoustic
models is not readily available. This work aims to investigate the impact of
dataset size variations on the performance of various GMM-HMM Acoustic Models
and their respective computational costs.
In Automatic Speech Recognition, GMM-HMM had been widely used for acoustic
modelling. With the current advancement of deep learning, the Gaussian Mixture
Model (GMM) from acoustic models has been replaced with Deep Neural Network,
namely DNN-HMM Acoustic Models. The GMM models are widely used to create the
alignments of the training data for the hybrid deep neural network model, thus
making it an important task to create accurate alignments. Many factors such as
training dataset size, training data augmentation, model hyperparameters, etc.,
affect the model learning. Traditionally in machine learning, larger datasets
tend to have better performance, while smaller datasets tend to trigger
over-fitting. The collection of speech data and their accurate transcriptions
is a significant challenge that varies over different languages, and in most
cases, it might be limited to big organizations. Moreover, in the case of
available large datasets, training a model using such data requires additional
time and computing resources, which may not be available. While the data about
the accuracy of state-of-the-art ASR models on open-source datasets are
published, the study about the impact of the size of a dataset on acoustic
models is not readily available. This work aims to investigate the impact of
dataset size variations on the performance of various GMM-HMM Acoustic Models
and their respective computational costs.
Impact of Dataset on Acoustic Models for Automatic Speech Recognition
http://arxiv.org/abs/2203.13590v1