A Review of Deep Learning Techniques for Speech Processing

The field of speech processing has undergone a transformative shift with the advent of deep learning. The use of multiple processing layers has enabled the creation of models capable of extracting intricate features from speech data. This development has paved the way for unparalleled advancements in speech recognition, text-to-speech synthesis, automatic speech recognition, and emotion recognition, propelling the performance of these tasks to unprecedented heights. The power of deep learning techniques has opened up new avenues for research and innovation in the field of speech processing, with far-reaching implications for a range of industries and applications. This review paper provides a comprehensive overview of the key deep learning models and their applications in speech-processing tasks. We begin by tracing the evolution of speech processing research, from early approaches, such as MFCC and HMM, to more recent advances in deep learning architectures, such as CNNs, RNNs, transformers, conformers, and diffusion models. We categorize the approaches and compare their strengths and weaknesses for solving speech-processing tasks. Furthermore, we extensively cover various speech-processing tasks, datasets, and benchmarks used in the literature and describe how different deep-learning networks have been utilized to tackle these tasks. Additionally, we discuss the challenges and future directions of deep learning in speech processing, including the need for more parameter-efficient, interpretable models and the potential of deep learning for multimodal speech processing. By examining the field's evolution, comparing and contrasting different approaches, and highlighting future directions and challenges, we hope to inspire further research in this exciting and rapidly advancing field

Recent Trends in Computational Intelligence

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications

Dual-level segmentation method for feature extraction enhancement strategy in speech emotion recognition

The speech segmentation approach could be one of the significant factors contributing to a Speech Emotion Recognition (SER) system's overall performance. An utterance may contain more than one perceived emotion, the boundaries between the changes of emotion in an utterance are challenging to determine. Speech segmented through the conventional fixed window did not correspond to the signal changes, due to the random segment point, an arbitrary segmented frame is produced, the segment boundary might be within the sentence or in-between emotional changes. This study introduced an improvement of segment-based segmentation on a fixed-window Relative Time Interval (RTI) by using Signal Change (SC) segmentation approach to discover the signal boundary concerning the signal transition. A segment-based feature extraction enhancement strategy using a dual-level segmentation method was proposed: RTI-SC segmentation utilizing the conventional approach. Instead of segmenting the whole utterance at the relative time interval, this study implements peak analysis to obtain segment boundaries defined by the maximum peak value within each temporary RTI segment. In peak selection, over-segmentation might occur due to connections with the input signal, impacting the boundary selection decision. Two approaches in finding the maximum peaks were implemented, firstly; peak selection by distance allocation, and secondly; peak selection by Maximum function. The substitution of the temporary RTI segment with the segment concerning signal change was intended to capture better high-level statistical-based features within the signal transition. The signal's prosodic, spectral, and wavelet properties were integrated to structure a fine feature set based on the proposed method. 36 low-level descriptors and 12 statistical features and their derivative were extracted on each segment resulted in a fixed vector dimension. Correlation-based Feature Subset Selection (CFS) with the Best First search method was applied for dimensionality reduction before Support Vector Machine (SVM) with Sequential Minimal Optimization (SMO) was implemented for classification. The performance of the feature fusion constructed from the proposed method was evaluated through speaker-dependent and speaker-independent tests on EMO-DB and RAVDESS databases. The result indicated that the prosodic and spectral feature derived from the dual-level segmentation method offered a higher recognition rate for most speaker-independent tasks with a significant improvement of the overall accuracy of 82.2% (150 features), the highest accuracy among other segmentation approaches used in this study. The proposed method outperformed the baseline approach in a single emotion assessment in both full dimensions and an optimized set. The highest accuracy for every emotion was mostly contributed by the proposed method. Using the EMO-DB database, accuracy was enhanced, specifically, happy (67.6%), anger (89%), fear (85.5%), disgust (79.3%), while neutral and sadness emotion obtained a similar accuracy with the baseline method (91%) and (93.5%) respectively. A 100% accuracy for boredom emotion (female speaker) was observed in the speaker-dependent test, the highest single emotion classified, reported in this study

Deep Learning: Our Miraculous Year 1990-1991

In 2020, we will celebrate that many of the basic ideas behind the deep learning revolution were published three decades ago within fewer than 12 months in our "Annus Mirabilis" or "Miraculous Year" 1990-1991 at TU Munich. Back then, few people were interested, but a quarter century later, neural networks based on these ideas were on over 3 billion devices such as smartphones, and used many billions of times per day, consuming a significant fraction of the world's compute.Comment: 37 pages, 188 references, based on work of 4 Oct 201

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

STUDY OF HAND GESTURE RECOGNITION AND CLASSIFICATION

To recognize different hand gestures and achieve efficient classification to understand static and dynamic hand movements used for communications.Static and dynamic hand movements are first captured using gesture recognition devices including Kinect device, hand movement sensors, connecting electrodes, and accelerometers. These gestures are processed using hand gesture recognition algorithms such as multivariate fuzzy decision tree, hidden Markov models (HMM), dynamic time warping framework, latent regression forest, support vector machine, and surface electromyogram. Hand movements made by both single and double hands are captured by gesture capture devices with proper illumination conditions. These captured gestures are processed for occlusions and fingers close interactions for identification of right gesture and to classify the gesture and ignore the intermittent gestures. Real-time hand gestures recognition needs robust algorithms like HMM to detect only the intended gesture. Classified gestures are then compared for the effectiveness with training and tested standard datasets like sign language alphabets and KTH datasets. Hand gesture recognition plays a very important role in some of the applications such as sign language recognition, robotics, television control, rehabilitation, and music orchestration