Stuttering, a prevalent neurodevelopmental disorder, profoundly affects fluent speech, causing involuntary interruptions and recurrent sound patterns. This study addresses the critical need for the accurate classification of stuttering types. The researchers introduce “TranStutter”, a pioneering Convolution-free Transformer-based DL model, designed to excel in speech disfluency classification. Unlike conventional methods, TranStutter leverages Multi-Head Self-Attention and Positional Encoding to capture intricate temporal patterns, yielding superior accuracy. In this study, the researchers employed two benchmark datasets: the Stuttering Events in Podcasts Dataset (SEP-28k) and the FluencyBank Interview Subset. SEP-28k comprises 28,177 audio clips from podcasts, meticulously annotated into distinct dysfluent and non-dysfluent labels, including Block (BL), Prolongation (PR), Sound Repetition (SR), Word Repetition (WR), and Interjection (IJ). The FluencyBank subset encompasses 4144 audio clips from 32 People Who Stutter (PWS), providing a diverse set of speech samples. TranStutter’s performance was assessed rigorously. On SEP-28k, the model achieved an impressive accuracy of 88.1%. Furthermore, on the FluencyBank dataset, TranStutter demonstrated its efficacy with an accuracy of 80.6%. These results highlight TranStutter’s significant potential in revolutionizing the diagnosis and treatment of stuttering, thereby contributing to the evolving landscape of speech pathology and neurodevelopmental research. The innovative integration of Multi-Head Self-Attention and Positional Encoding distinguishes TranStutter, enabling it to discern nuanced disfluencies with unparalleled precision. This novel approach represents a substantial leap forward in the field of speech pathology, promising more accurate diagnostics and targeted interventions for individuals with stuttering disorders.

Gestures were most likely utilised by our ancestors to communicate. Armstrong once stated that he believes movements using the hands were the earliest form of complex human communication. The beginning stage of human computer Interactions is a gesture recognition system. Here, we have designed a Dynamic Hand Gesture Recognition (HGR) System using a Neural Network, which can recognize the gesture using the Computer or Laptop’s Web Camera and do the corresponding tasks. The model has been divided into mainly three modules. Firstly, Module 1 is about building a CNN model which we use to predict the gestures. Our second module is about predicting the gesture through live video feed. The final module is about assigning a specified task for a particular predicted gesture. For the predicted gesture, we use PyAutoGUI module to control devices like mouse and keyboard. Finally, the system is made to do some desired tasks in response to the gestures and obtains 99.84% of accuracy.

The capacity to talk smoothly is typically affected by stuttering, a neuro-developmental speech disorder where the flow of speech is disrupted by involuntary pauses and repetition of sounds. Stuttering can be cured by identifying the type of stutter and providing proper speech guidance. Many approaches have been taken to classify stuttered speech via a computer aided process including Deep Learning models. But most of the works rely heavily on a large number of audio features to be extracted manually. Also, many past works use the UCLASS dataset that is much older and lacks in quality. This paper proposes a Deep Learning model using Bidirectional LSTM and Attention to classify five types of stuttering events – Block, Prolongation, Word Repetition, Sound Repetition and Interjection, by utilizing only Mel-spectrogram audio feature. The model is trained and tested on the SEP-28k and latest annotations of the FluencyBank dataset to evaluate the performance and achieves an overall 75% accuracy.