Application of Machine Learning and Deep Learning in Stuttering Recognition: A Bibliometric and Co-Occurrence Analysis Based Study

Nilanjan Banerjee1, Nilambar Sethi2, Samarjeet Borah3, Nikunja Mohan Modak4

1Department of Computer Science, GIET University, Odisha, India

2Department of Computer Science, GIET University, Odisha, India

3Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Sikkim, India

4Palpara Vidyamandir, Chakda, West Bengal, India

{1nilanjan.banerjee@giet.edu,2nilambar@giet.edu,3samarjeet.b@smit.smu.edu.in, 4nikunja.modak@gmail.com}

Abstract. Stuttering is an extensive speech syndrome affecting people worldwide, and it affects real-life communication and quality of life. The field of stuttering speech recognition holds great potential for research and advancement in the realm of speech signal processing. Machine learning has made significant progress in recent years, opening new avenues for enhancing stuttering detection and treatment methods. Deep learning methods have proven to be highly effective for accurately classifying stuttering, in line with current practices and trends. This study surveys the most current developments in the field of artificial intelligence (AI) relevant to stuttering speech recognition & presents an overview of a decade's worth of research on this field. The primary goal of these publications was to assess the effectiveness of stuttering speech recognition in Scopus, within the framework of current research trends and top nations. The focus is on the search syntax used in this field. The Scopus database was employed to conduct a search utilizing the keywords "stuttering speech recognition" in order to retrieve 53 records from 2015 to January 2024. The retrieved records were subsequently evaluated using the VOSviewer software.

Keywords: Stuttering; Machine learning; Deep learning; speech recognition; Scopus: Review

Introduction

Speech is a form of verbal communication used to articulate emotions, concepts, and perspectives. Stuttering is a form of communication disorder that causes disruptions in the smooth flow of speech. Individuals who stutter may struggle to articulate their thoughts and experiences, leading to challenges in everyday communication. Globally, over 70 million individuals are impacted by this issue, which accounts for approximately 1% of the world's population. The conventional method for evaluating stuttering involves manually tallying and categorizing instances of disruption in stuttered speech. However, this approach can vary depending on the individual speech-language pathologist (SLP) administering the assessment, resulting in a time-consuming and potentially inaccurate process [1]. Therefore, artificial intelligence plays a crucial role in the classification of dysfluencies for automatic stuttering recognition systems, providing valuable support to Speech Language Pathology (SLP). The study of stuttering speech recognition offers a promising avenue for research in the field of speech signal processing. This interdisciplinary field has garnered interest from various domains such as speech pathology, psychology, speech physiology, acoustics, and signal analysis over the past few decades. In previous decades, researchers have employed a range of traditional and machine-learning techniques to detect stuttering in speech. Our prior research [1] thoroughly evaluated and analyzed the major advancements made in this area. In the past, the field of stuttering research has primarily focused on recognizing the prolongation, interjection, and repetition types of stuttering. Most of the previous studies have utilized MFCC and LPC feature extraction methods for stuttering speech recognition. However, the emergence of three key classifiers - HMMs, SVM, and ANNs - has been notable in this field. In recent years, there has been a growing trend of utilizing deep learning models such as CNNs and LSTMs to achieve remarkable accuracy in this domain. Several significant systematic reviews, including our own, have been published in previous years, namely Lim Sin Chee et al. (2009) [2], Sakshi Gupta et al. (2019) [3], Shakeel A. Sheikh et al. (2022) [4], Liam Barrett et al. (2022) [5], and Raghad Alnashwan et al. (2023) [6], who conducted a comprehensive review that encompassed various aspects of stuttering identification, including different classification model based on different feature extraction methods on different datasets. The primary databases used include UCLASS [7], and SEP-28K [8].

This study critically reviews the literature on stuttering speech recognition in terms of speech by utilizing bibliometric and co-occurrence analysis. Co-occurrence analysis involves quantitatively examining paired data within a collection, while bibliometric analysis enables the measurement of relationships and impact of publications in a specific field of research. This research examined a total of 53 records related to "stuttering speech recognition" from 2015 to January 2024. The Scopus database was utilized to search for these records using the keywords "stuttering speech recognition" and "analyzed with VOSviewer software”. Part 2 of this article includes the ethics statement, study design, data collection, and visualization. In Section 3, the findings of our research are presented. To analyze the information from the Scopus database, we utilized both Excel and the VOSviewer tool. Section 4 presents an overview of the discussion within this study, while Section 5 presents the conclusions drawn from the research.

Method

The methods for the analysis have been discussed in the following sections.

Design

The present study is based on a descriptive and bibliometric analysis of a literature database, providing a strong foundation for our research.

Collecting Information

The Scopus data retrieved covers the period from 2015 to January 2024. When the search parameters “(TITLE-ABS-KEY(stuttering) AND TITLE-ABS-KEY("machine learning") OR TITLE-ABS-KEY("deep learning”)" were entered into the search field. The search engine of SCOPUS discovers a total 53 number of documents including 32 articles, 13 conference papers,	three conference reviews, two reviews, one editorial, one letter and one short survey. Using the CSV file format and VOSviewer, the data was analyzed for bibliometric purposes. Before conducting the analysis, measures were taken to ensure the reliability and consistency of the data. This included checking for any discrepancies in country names and titles, such as abbreviations or acronyms [9].

Visual Representation

The data retrieved from the Scopus database was analyzed through the utilization of Excel and the VOSviewer software.

Results

The search engine of SCOPUS discovers a total 53 number of documents including 32 articles, 13 conference papers, three conference reviews, two reviews, one editorial, one letter and one short survey. This distribution is represented in figure1.

Fig. 1. Distribution of document types

Figure 2 demonstrates the systematic organization of documents within Scopus in chronological order. According to Figure 2, it is evident that 2022 holds significant importance as the maximum number of research studies have been published.

Fig. 2. Number of documents published per year

Most of the research has primarily focused on the fields of Computer Science, Engineering, and Mathematics. However, there have also been studies conducted in other areas such as Medicine, Neuroscience, Social Sciences, Physics and Astronomy, Arts and Humanities, Biochemistry & Genetics, Decision Sciences, and others (see Figure 3).

Fig. 3. The subject area of the publications

The field has historically received minimal contributions, however, in recent years it has experienced considerable growth and development. Table 1 presents a comprehensive overview of the contributions made by authors, institutions, and countries in research on Stuttering Speech Recognition. The results are depicted as the Number of Documents (ND) per category. The results of the author and institution-based study are outlined in the table. Several countries have significantly contributed to this field, including India, the US, France, and the UK. Additionally, China, Canada, Italy, Saudi Arabia, Australia, and Belgium have also reported progress in this area. The diagram shown in Figure 4 illustrates the country-specific segmentation of articles in the Scopus Database.

Table 1. Top Ten Leading Authors, Institutions, and Countries

Author	ND	Institution	ND	Country	ND
Hirsch, F.	4	CNRS Centre National de la Recherche Scientifique	4	India	13
Ouni, S.	4	Universite Paul-Valery Montpellier III	4	United States	12
Sahidullah, M.	4	INRIA Institut National de Recherche en Informatique et en Automatique	4	France	6
Sheikh, S.A.	4	LORIA Laboratoire Lorrain de Recherche en Informatique et ses Applications	4	United Kingdom	6
Alharbi, S.	3	Université de Lorraine	4	China	4
Brumfitt, S.	3	The University of Sheffield	3	Canada	3
Green, P.	3	B.S.Abdur Rahman University	3	Italy	3
Hasan, M.	3	Loughborough University	2	Saudi Arabia	3
Simons, A.J.H.	3	Vanderbilt University	2	Australia	2
Abdul Waheed, S.	2	Wayne State University	2	Belgium	2

Fig. 4. Country-specific Segmentation of Articles in the Scopus Database

Density Visualization of Co-Occurrences

VOSviewer is a sophisticated software tool designed for the creation of maps using network data. Additionally, it offers advanced visualization and exploration options for these maps. VOSviewer presents three primary visualizations: the network visualization, overlay visualization, and density visualization [10]. We demonstrate the density visualization. The item density visualization displays items using their labels, like the network and overlay visualizations. Each point in the visualization is assigned a specific color to represent the density of items at that location. The colors span from blue to green, followed by yellow and finally red. Points with a higher number of neighboring items and higher weights will have a color closer to red & yellow, while points with fewer neighboring items and lower weights will have a color closer to blue. The term "stuttering" is the most prevalent keyword in 53 SCOPUS documents, as revealed in Table 2. It is closely followed by "deep learning" and "machine learning," highlighting their significant usage in the research field. Figure 4 presents a density visualization of the co-occurrence patterns for frequently utilized keywords in 53 SCOPUS documents. It is shown that the most frequently used keywords such as “Stuttering”, “Deep Learning”, “Machine Learning”, “Speech”, and “Speech Recognition” are highlighted in the red color area on the density visualization map (figure 5). Keywords such as “Speech Disorders”, “Human”, “Learning Systems”, “Article”, and “Artificial Intelligence” fall into the moderate usage category, represented by yellow. The remaining keywords have lower frequency of use, depicted in green on the density visualization map. This data supports the observation that these terms are of relevance and importance within this field of study.

Table 2. Mostly used Keywords

Keywords	Frequencies	Keywords	Frequencies
Stuttering	25	Classification (of Information)	6
Deep Learning	23	Controlled Study	6
Machine Learning	21	Feature Extraction	6
Speech	19	Automatic Speech Recognition	5
Speech Recognition	18	Classification	5
Speech Disorders	13	Disfluencies	5
Human	12	Features Extraction	5
Learning Systems	12	Female	5
Article	9	Long Short-term Memory	5
Artificial Intelligence	9	Machine Learning Approaches	5
Humans	7	Male	5
Speech Disorder	7	Stuttering Detection	5

Fig. 5. Density Visualization of Co-Occurrences of Keywords

Table 3 presents the titles, authors, publication years, and citation details for 35 out of 53 documents, including articles, review papers, and surveys. The density visualization of co-occurrences of keywords used in 35 documents is illustrated in figure 6. Keywords such as “Stuttering”, “Speech”, “Machine Learning”, and “Deep Learning”, are highlighted in the red color area on the density visualization map, indicating that they are the most frequently used keywords.

Table 3. List of 35 Articles, Review Papers, and Survey

Titles	Authors	Year	Citation
Rediscovering Automatic Detection of Stuttering and Its Subclasses through Machine Learning—The Impact of Changing Deep Model Architecture and Amount of Data in the Training Set [11]	Filipowicz P.; Kostek B.	2023	1
Solving inventory routing with transshipment and substitution under dynamic and stochastic demands using genetic algorithm and deep reinforcement learning [12]	Achamrah F.E.; Riane F.; Limbourg S.	2022	14
A machine learning approach for managing the potential risk of odds of developmental stuttering [13]	Abdul Waheed S.; Abdul Khader P.S.	2021	3
Feature extraction from behavioral styles of children for prediction of severity of stuttering using historical stuttering data [14]	Abdul Waheed S.; Khader P.S.A.; Khan A.A.; Sathick J.	2022	2
Systematic Review of Machine Learning Approaches for Detecting Developmental Stuttering [5]	Barrett L.; Hu J.; Howell P.	2022	13
Advancing Stuttering Detection via Data Augmentation, Class-Balanced Loss and Multi-Contextual Deep Learning [15]	Sheikh S.A.; Sahidullah M.; Hirsch F.; Ouni S.	2023	3
Analyzing the Disfluency of Reading Tasks of Persons Who Stutter Based on Deep Learning and Word Embedding [16]	Song S.; Chon H.; Lee S.B.	2020	2
Deep learning bidirectional LSTM based detection of prolongation and repetition in stuttered speech using weighted MFCC [17]	Gupta S.; Shukla R.S.; Shukla R.K.; Verma R.	2020	9
Sequence labeling to detect stuttering events in read speech [18]	Alharbi S.; Hasan M.; Simons A.J.H.; Brumfitt S.; Green P.	2020	14
Science and Technology Are the Collective Wisdom of Our Predecessors. It Is Our Mission-the Researchers of Today-to Make Them Even Better [19]	Kameoka H.	2023	0
Identifying stuttering using deep learning [20]	Tibrewal V.; Haque M.M.; Pandey A.; Manimozhi M.	2019	1
TranStutter: A Convolution-Free Transformer-Based Deep Learning Method to Classify Stuttered Speech Using 2D Mel-Spectrogram Visualization and Attention-Based Feature Representation [21]	Basak K.; Mishra N.; Chang H.-T.	2023	0
Performance analysis of machine learning for arbitrary downsizing of pre-encoded HEVC video [22]	Van L.P.; De Praeter J.; Van Wallendael G.; De Cock J.; Van De Walle R.	2015	9
Multimodal explainable AI predicts upcoming speech behavior in adults who stutter [23	Das A.; Mock J.; Irani F.; Huang Y.; Najafirad P.; Golob E.	2022	2
A novel attention model across heterogeneous features for stuttering event detection [24]	Al-Banna A.-K.; Fang H.; Edirisinghe E.	2024	0
Machine learning for stuttering identification: Review, challenges and future directions [4]	Sheikh S.A.; Sahidullah M.; Hirsch F.; Ouni S.	2022	10
An fNIRS-based feature learning and classification framework to distinguish hemodynamic patterns in children who stutter [25]	Hosseini R.; Walsh B.; Tian F.; Wang S.	2018	27
A Generic AI-Based Technique for Assessing Student Performance in Conducting Online Virtual and Remote Controlled Laboratories [26]	Abd El-Haleem A.M.; Eid M.M.; Elmesalawy M.M.; Hosny H.A.H.	2022	1
Phenome risk classification enables phenotypic imputation and gene discovery in developmental stuttering [27]	Shaw D.M.; Polikowsky H.P.; Pruett D.G.; Chen H.-H.; Petty L.E.; Viljoen K.Z.; Beilby J.M.; Jones R.M.; Kraft S.J.; Below J.E.	2021	7
Identifying developmental stuttering and associated comorbidities in electronic health records and creating a phenome risk classifier [28]	Pruett D.G.; Shaw D.M.; Chen H.-H.; Petty L.E.; Polikowsky H.G.; Kraft S.J.; Jones R.M.; Below J.E.	2021	15
FluentNet: End-to-End Detection of Stuttered Speech Disfluencies with Deep Learning [29]	Kourkounakis T.; Hajavi A.; Etemad A.	2021	35
Functional neural circuits that underlie developmental stuttering [30]	Qiao J.; Wang Z.; Zhao G.; Huo Y.; Herder C.L.; Sikora C.O.; Peterson B.S.	2017	20
Computational Intelligence-Based Stuttering Detection: A Systematic Review [6]	Alnashwan R.; Alhakbani N.; Al-Nafjan A.; Almudhi A.; Al-Nuwaiser W.	2023	0
Fast 3D Visualization of Massive Geological Data Based on Clustering Index Fusion [31]	Zhang Y.-H.; Wen C.; Zhang M.; Xie K.; He J.-B.	2022	12
Prioritizing surveillance of nipah virus in India [32]	Plowright R.K.; Becker D.J.; Crowley D.E.; Washburne A.D.; Huang T.; Nameer P.O.; Gurley E.S.; Han B.A.	2019	59
Intelligent stuttering speech recognition: A succinct review [1]	Banerjee N.; Borah S.; Sethi N.	2022	3
Machine learning approach to analyze the impact of demographic and linguistic features of children on their stuttering [33]	Waheed S.A.; Matheen M.A.; Hussain S.; Lodhi A.K.; Maboobatcha G.S.	2023	2
Psychophysiological Arousal in Young Children Who Stutter: An Interpretable AI Approach [34]	Sharma H.; Xiao Y.; Tumanova V.; Salekin A.	2022	0
Effect of speech segment samples selection in stutter block detection and remediation [35]	Arbajian P.; Hajja A.; Raś Z.W.; Wieczorkowska A.A.	2019	4
Regularized Deep LSTM Autoencoder for Phonological Deviation Assessment [36]	Pravin S.C.; Palanivelan M.	2021	9
Language Sample Analysis With TalkBank: An Update and Review [37]	MacWhinney B.; Fromm D.	2022	1
Stuttering Disfluency Detection Using Machine Learning Approaches [38]	Al-Banna A.-K.; Edirisinghe E.; Fang H.; Hadi W.	2022	3
Stuttering detection using speaker representations and self-supervised contextual embeddings [39]	Sheikh S.A.; Sahidullah M.; Hirsch F.; Ouni S.	2023	1
Acoustic analysis in stuttering: a machine-learning study [40]	Asci F.; Marsili L.; Suppa A.; Saggio G.; Michetti E.; Di Leo P.; Patera M.; Longo L.; Ruoppolo G.; Del Gado F.; Tomaiuoli D.; Costantini G.	2023	1
Sex Differences in Functional Brain Networks for Language [41]	Xu M.; Liang X.; Ou J.; Li H.; Luo Y.-J.; Tan L.H.	2020	17

Fig. 6. Density Visualization of Co-Occurrences of Keywords used In 35 Documents of Table 3

Discussion

This work demonstrates how stuttering speech recognition has recently attracted a lot of research attention. Over the past few decades, various machine-learning models have been utilized in the realm of stuttered speech recognition for analysis and classification. In recent years, deep learning algorithms have gained prominence over traditional machine learning methods in this field, as discussed briefly in this study. Previous research has heavily relied on using MFCC and LPC features to train and evaluate these models. Most studies have focused on detecting prolongation and repetition types of stuttering, with some attention given to interjection types. However, there is a lack of research on classifying other forms of stuttering such as broken words, revisions, and incomplete phrases [1].

The thorough examination of keyword co-occurrence in the title serves as compelling evidence that this subject of investigation is continuously expanding. The purpose of this research is to give a thorough analysis of the research of stuttering speech detection, covering the period from 2015 to January 2024. The SCOPUS search engine identified a total of 53 documents, comprised of 32 articles, 13 conference papers, three conference reviews, two reviews, one editorial, one letter and one short survey. According to the findings of this research, the following topics have the greatest impact on this investigation: "Stuttering", "Deep Learning", "Machine Learning", "Speech", and "Speech Recognition". These areas are also the primary focus of this study. It seems that this area of signal processing will continue to significantly advance in the foreseeable future. The field has seen limited contributions in the past but has undergone significant expansion in recent years. The year 2022 holds significant importance as the highest number of research studies have been published in this field. Notably, countries such as India and the US have made impact contributions to this field. The findings of this investigation should encourage more research and development in this area.

Conclusion

The subject of identifying stuttering speech signals has received considerable attention in contemporary research. This paper aims to achieve two primary goals: provide a succinct summary of current studies on stuttering speech recognition and propose suggestions for future research in this field. The results of this investigation clearly indicate that the following areas, namely "Stuttering", "Deep Learning," "Machine Learning", "Speech", and "Speech Recognition", had the most notable impact on this inquiry. It is evident that signal processing in these areas will continue to experience significant growth in the coming years. The field has seen limited contributions in the past but has undergone significant expansion in recent years. Significantly, countries like India and the United States have made noteworthy contributions to this domain. This analysis has the potential to inspire and motivate researchers within this field.

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