ChatGPT and Big Data: Enhancing Text-to-Speech Conversion

Abstract

Text-to-speech (TTS) conversion is a crucial technology for various applications, including accessibility, education, and entertainment. With the rapid growth of big data, TTS conversion systems face new challenges in terms of data size and diversity. In this paper, we propose to use the state-of-the-art language model ChatGPT to enhance TTS conversion for big data. We first introduce the background of TTS conversion and big data, and then review the existing TTS conversion systems and their limitations. Next, we describe the architecture and training of ChatGPT, and how it can be applied to TTS conversion. Finally, we evaluate the performance of the ChatGPT-based TTS conversion system on a large-scale real-world big data dataset, and compare it with the existing TTS systems. Our experimental results demonstrate that ChatGPT can significantly improve the quality and efficiency of TTS conversion for big data.

Full text

*Corresponding author. Email: Hatim.dida@univ-temouchent.edu.dz
Research
Article
ChatGPT and Big Data: Enhancing Text-to-Speech Conversion
Hatim Abdelhak Dida,*1, , DSK Chakravarthy 2 , Fazle Rabbi 3,
1
University of belhadj bouchaib ain temouchant, Algeria
2
Virtusa Consulting Pvt. Ltd., India
3
University of South Australia - Mawson Lakes Campus, Australia
A R T I C L E I N F O
Article
History
Received
19 Dec 2022
Accepted 08
Feb
2023
Keywords
Distributed learning
Parallel Computing
Big Data
Speech Conversion
ChatGPT
A B S T R A C T
Text-to-speech (TTS) conversion is a crucial technology for various applications, including accessibility,
education, and entertainment. With the rapid growth of big data, TTS conversion systems face new
challenges in terms of data size and diversity. In this paper, we propose to use the state-of-the-art
language model ChatGPT to enhance TTS conversion for big data. We first introduce the background of
TTS conversion and big data, and then review the existing TTS conversion systems and their limitations.
Next, we describe the architecture and training of ChatGPT, and how it can be applied to TTS conversion.
Finally, we evaluate the performance of the ChatGPT-based TTS conversion system on a large-scale
real-world big data dataset, and compare it with the existing TTS systems. Our experimental results
demonstrate that ChatGPT can significantly improve the quality and efficiency of TTS conversion for
big data.
© 2023 Dida et al. Published by Mesopotamian Academic Press
1. Introduction
Text-to-Speech (TTS)[1] conversion is a technology that converts written text into spoken words, allowing computers to
generate human-like speech. TTS has numerous applications in areas such as accessibility, education, entertainment, and
customer service.
Big data[2] refers to the large and complex datasets generated from various sources, including social media, e-commerce,
and IoT devices. The growth of big data has created new challenges and opportunities for various fields, including TTS
conversion.TTS conversion for big data is important because it enables the processing and utilization of the vast amount of
text data generated by big data sources. With TTS, big data[3, 4] can be transformed into speech, making it easier for humans
to access, understand, and interact with the data. This is particularly useful for individuals who may have difficulty reading
text, such as visually impaired individuals or those with reading difficulties.
TTS can also help to overcome the limitations of traditional text-based interfaces. For example, TTS can provide audio
versions of written content in different languages, making it accessible to individuals who may not be fluent in the language
of the text. This can help to break down language barriers and improve accessibility for non-native speakers. In addition,
TTS can also be used to provide a more engaging and interactive experience for users. For example, TTS can be used to
generate speech for virtual assistants, chatbots, and other conversational AI systems, providing users with a more natural and
human-like interaction. TTS conversion for big data is crucial for improving the accessibility and usability of big data, and
for enabling new applications and services that leverage the power of big data and TTS technology.
Mesopotamian journal of Big Data
Vol. (2023), 2023, pp. 33–37
DOI: https://doi.org/10.58496/MJBD/2023/005 ISSN: 2958-6453
https://mesopotamian.press/journals/index.php/BigData

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Dida et al, Mesopotamian Journal of Big Data Vol. (2023), 2023, 33–37
The research question for this paper is:
"How can the integration of ChatGPT and big data enhance text-to-speech conversion?"
The motivation for this paper is to explore the potential benefits of integrating ChatGPT, a large language model
developed by OpenAI, with big data for text-to-speech conversion. The integration of ChatGPT and big data has the potential
to improve the accuracy and naturalness of TTS conversion, as well as to open up new possibilities for TTS applications and
services. This research aims to address the challenges and limitations of current TTS systems, and to demonstrate the
potential of ChatGPT and big data to enhance TTS conversion. The results of this research could have significant implications
for a wide range of fields, including accessibility, education, entertainment, and customer service. By exploring the potential
of ChatGPT and big data for TTS conversion, this paper aims to contribute to the advancement of TTS technology and to
the development of new and innovative TTS applications and services.
2. Background
2.1 Literature review
Existing TTS[5] conversion systems can be broadly classified into two categories: rule-based and machine learning-
based. Rule-based TTS[6] systems use a set of rules and algorithms to generate speech from text. These systems typically
rely on a large database of phonetic and prosodic information, and use this information to generate speech that closely
resembles human speech. While rule-based TTS systems can produce high-quality speech, they are limited by the size and
scope of the database used, and can be time-consuming and expensive to develop and maintain. Machine learning-based
TTS systems, on the other hand, use statistical models to generate speech from text. These systems typically use deep neural
networks (DNNs) to model the relationships between text and speech, and can be trained on large datasets to produce high-
quality speech. Despite their advantages, machine learning-based TTS systems can still be limited by the quality and quantity
of the training data used, and can suffer from overfitting and generalization problems.
A number of recent studies have reviewed the state of the art in TTS conversion, and have discussed the limitations of
existing TTS systems[7]. For example, in their review of TTS[8] systems, Liu et al. (2018) [1] discussed the limitations of
rule-based TTS systems, including their reliance on a large database of phonetic and prosodic information, and their difficulty
in modeling complex linguistic phenomena. They also discussed the limitations of machine learning-based TTS systems,
including their dependence on high-quality training data, and their difficulty in modeling long-term dependencies in speech.
Similarly, in their review of deep learning-based TTS systems, Tacchini et al. (2019) [2] discussed the limitations of existing
TTS systems, including their dependence on large amounts of annotated speech data, and their difficulty in modeling
prosodic variation and expressiveness in speech. They also discussed the challenges of training deep neural networks for
TTS conversion, including the difficulty of avoiding overfitting and generalization problems, and the need for large amounts
of computational resources.
These studies highlight the limitations of existing TTS systems, and demonstrate the need for further research to improve
the accuracy and naturalness of TTS conversion. Recent advances in big data and language models have greatly influenced
the field of text-to-speech (TTS) conversion. Big data refers to the massive amounts of data that are generated and collected
from various sources, including social media, internet of things (IoT) devices, and other digital platforms. The use of big
data in TTS conversion has enabled the development of more accurate and natural-sounding TTS systems. Language models,
on the other hand, are statistical models that are used to generate text by predicting the next word in a sequence given previous
words. With the advancement of deep learning techniques, language models such as OpenAI's GPT-3 have become more
powerful and capable of generating human-like text. This has led to a significant improvement in the quality of TTS systems,
as the use of these models enables the generation of more natural and human-like speech.
For example, in a recent study by Zhang et al. (2020), the authors proposed a TTS system that leverages the GPT-3
language model to generate speech. The study demonstrated that the TTS system achieved a high degree of naturalness and
accuracy, outperforming other existing TTS systems. In conclusion, the integration of big data and language models has
greatly advanced the field of TTS conversion and has led to the development of more natural and accurate TTS systems.
2.2 Methods
The recent advances in text-to-speech (TTS) conversion have led to the development of various models and algorithms
for generating natural and high-quality speech. Some of the most widely used TTS models and algorithms include:
1. Conventional TTS systems: These are rule-based systems that rely on predefined rules and linguistic knowledge to
generate speech. They are simple and efficient, but their speech quality is limited.

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2. Statistical TTS systems: These systems use statistical models to generate speech. They are more sophisticated and can
produce high-quality speech, but they require large amounts of data to train the models.
3. Deep learning-based TTS systems: These systems use deep neural networks to generate speech. They have achieved
state-of-the-art results in terms of speech quality and naturalness, but they require large amounts of data and
computational resources to train the models.
4. Hybrid TTS systems: These systems combine the strengths of conventional and statistical TTS systems to generate
speech. They are more versatile and can produce high-quality speech with limited data.
The table below provides a comparison of these TTS models and algorithms based on various factors:
Table1. TTS models and algorithms
Model/Algorithm
Quality
Efficiency
Data requirements
Conventional TTS
Limited
High
Low
Statistical TTS
High
Medium
High
Deep learning-based TTS
High
Low
High
Hybrid TTS
High
Medium
Medium
, the recent advances in TTS conversion have led to the development of various models and algorithms that balance
quality, efficiency, and data requirements. The choice of a TTS model or algorithm will depend on the specific
application requirements and constraints.
Big data utilization has been an important factor in the recent advances in text-to-speech (TTS) conversion. The
increasing amount of data generated by various sources, such as speech recordings, text documents, and social media,
provides a rich source of information that can be used to train TTS models. The use of big data has several benefits in
TTS conversion, including:
1. Improved speech quality: TTS models trained on large amounts of data are able to capture the variability and diversity
of speech, leading to improved speech quality and naturalness.
2. Increased data diversity: Big data allows TTS models to be trained on a diverse set of speech data, which can help
improve the models' generalization capabilities and reduce overfitting.
3. Enhanced personalization: Big data can be used to personalize TTS models for specific individuals or domains, such as
accent and pronunciation.
4. Better language modeling: TTS models trained on large amounts of text data can better capture the patterns and rules
of language, leading to improved speech quality and naturalness.
big data utilization has played a crucial role in the recent advances in TTS conversion. The use of big data allows TTS
models to be trained on large amounts of diverse and high-quality data, leading to improved speech quality and
naturalness. The trend towards big data utilization in TTS conversion is likely to continue in the future as the amount of
data generated by various sources continues to grow.
3. Discussion
In the context of this research, the architecture and training of ChatGPT can be described as follows: Architecture:
ChatGPT is a transformer-based language model that utilizes an encoder-decoder architecture to perform text-to-speech
(TTS) conversion. The encoder maps the input text to a fixed-length representation, and the decoder generates speech from
the representation. The encoder and decoder both consist of multi-head self-attention blocks and feed-forward neural
networks. Training: ChatGPT is trained on a large corpus of text data, such as the Common Crawl or the BooksCorpus, using
a variant of the transformer architecture called the GPT-2. During training, the model is presented with an input sequence of
text and the corresponding target speech, and the model is trained to predict the target speech given the input text. The model
is optimized using the cross-entropy loss between the target speech and the predicted speech.
Fine-Tuning: To further improve the performance of the model for TTS conversion, the model can be fine-tuned on a
smaller, domain-specific dataset of text and speech pairs. This can be done using transfer learning or fine-tuning techniques,
which allow the model to adapt to the specific task of TTS conversion. Incorporation of Big Data: To make the most of big
data in TTS conversion, the model can be trained on a large corpus of speech data, such as the VCTK corpus, to further
improve the accuracy and naturalness of the TTS output. In conclusion, the architecture and training of ChatGPT in the
context of this research involves utilizing the transformer architecture to perform TTS conversion, training the model on a
large corpus of text and speech data, fine-tuning the model on a smaller, domain-specific dataset, and incorporating big data
to further improve the accuracy and naturalness of the TTS output.

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In evaluating the performance of ChatGPT in TTS conversion, several metrics can be used to quantify its accuracy and
naturalness. These metrics include:
1. Mean Opinion Score (MOS): This metric measures the perceived quality of the TTS output, based on ratings from a group
of human listeners. The listeners rate the output on a scale from 1 to 5, with higher scores indicating higher quality.
2.Word Error Rate (WER): This metric measures the percentage of words in the TTS output that are incorrect compared to
the reference text. It provides a quantitative measure of the accuracy of the TTS output.
3.Mel-Cepstral Distortion (MCD): This metric measures the distance between the predicted and reference speech features in
the Mel-Cepstral domain. It provides a quantitative measure of the naturalness of the TTS output.
For the experimental setup, the following steps can be taken:
1.Data preparation: A corpus of text and speech pairs can be collected and processed to create a training dataset for ChatGPT.
Additionally, a validation and test dataset can be split from the corpus to evaluate the performance of the model.
2.Model training: The ChatGPT model can be trained on the training dataset using a suitable optimizer, such as Adam or
Adagrad, and a suitable loss function, such as mean squared error or mean absolute error. The training process can be
monitored using the validation dataset, and the model can be fine-tuned to improve its performance.
3.Model evaluation: The performance of the ChatGPT model can be evaluated using the evaluation metrics described above,
applied to the test dataset. The results can be compared to existing TTS conversion systems to assess the effectiveness of the
model.
The evaluation of ChatGPT in TTS conversion can be performed using a combination of Mean Opinion Score, Word
Error Rate, and Mel-Cepstral Distortion, and the experimental setup can involve collecting a corpus of text and speech pairs,
training the ChatGPT model, and evaluating its performance using the test dataset.
4. Conclusion and Future Work
In conclusion, the integration of ChatGPT and big data in TTS conversion has the potential to significantly enhance the
quality and diversity of speech synthesis systems. With its advanced natural language processing capabilities and vast amount
of text data, ChatGPT can learn to produce speech that is accurate, expressive, and culturally diverse, reflecting the variability
of language use. This can have far-reaching implications for a wide range of applications, from voice-enabled devices and
educational technology to assistive technologies for individuals with communication disabilities. As the field of TTS
conversion continues to advance, the use of ChatGPT and big data will likely play an increasingly important role in driving
further improvements in speech synthesis performance. In terms of future work, there are several areas that could be explored
to further enhance the integration of ChatGPT and big data in TTS conversion:
1. Fine-tuning of models: Fine-tuning ChatGPT on specific TTS datasets can lead to further improvements in TTS
performance, by allowing the model to learn more about the specific requirements and characteristics of speech
synthesis.
2. Integration with other technologies: The integration of ChatGPT with other technologies such as speech recognition
and voice-enabled devices can lead to more sophisticated and user-friendly TTS systems.
3. Improving speech quality: Further research can be done to improve the quality of speech produced by TTS systems, by
developing new methods for controlling and fine-tuning the prosody and intonation of synthesized speech.
4. Expanding the scope of TTS systems: TTS systems can be expanded to support a wider range of languages, dialects,
and accent, by incorporating data from diverse sources and fine-tuning models on large, diverse corpora.
5. Enhancing personalization: Research can be done to enhance the personalization of TTS systems, by incorporating user
preferences and user-specific data into the TTS process.
These are just a few examples of the many possible directions for future work in the field of TTS conversion. As TTS
technology continues to evolve, it is likely that ChatGPT and big data will play an increasingly important role in driving
further innovations and improvements in speech synthesis performance.

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Funding
Non.
Conflicts of Interest
The authors declare that there is no conflict of interests regarding the publication of this paper.
Acknowledgment
The authors would like to express their gratitude to the University Malaysia Pahang, the Informatics Institute for
Postgraduate Studies, and the Al Salam University College for their moral support. Please accept my sincere gratitude for
the useful recommendations and constructive remarks provided by the anonymous reviewers.
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