Intelligent speech signal processing methods have increasingly replaced the conventional analog signal processing methods in several applications, including speech analysis and processing, telecommunications, and tracking. These intelligent speech signal processing approaches support different areas in a variety of everyday problems, multimedia communications, industrial automation, and biometrics. Incorporating different signal processing approaches, such as signal analysis using an analytical signal description, can be combined for efficient speech detection. In intelligent systems, pattern recognition and machine learning methods are vital tools for reasoning under uncertainty. They help to extract significant information from massive data in an automated fashion using statistical and computational methods. This domain is related to probability, statistics, optimization methods, and control theory. The focus is on providing solutions for tasks at which intelligence is inevitably essential. Application domains include computer vision, speech processing, natural language processing, man–machine interfaces, expert systems, and robotics, etc. Typically, there are general attributes that should be included in the intelligent signal processing system, namely nonlinearity, adaptively, and robustness. A speech signal processing device that operates in a nonstationary environment can be considered intelligent once it is able to explore the information content of its input in an efficient mode and at all times.

This book highlights researchers from machine learning, data analysis, data management, and speech processing provider fields. The authors sought trends and techniques in intelligent speech signal processing and data analysis to spotlight scientific breakthroughs in applied applications. The book includes 10 chapters. In Chapter 1, Santosh focuses on speech recognition/processing/synthesis in healthcare. He provides detailed information about how speech synthesis impacts healthcare and how it also impacts its business model. In Chapter 2, Passricha and Aggarwal discuss end-to-end acoustic modeling using the Conventional Neural Network (CNN) to establish the relationship between the raw speech signal and phones in a data-driven manner. This system has superior performance compared to the traditional cepstral feature-based systems, however, it requires a large number of parameters. In Chapter 3, Singh et al. propose a real-time DSP-based system for voice activity detection and background noise reduction. In Chapter 4, Sad et al. introduce a novel system to disambiguate conflict classification results in audio visual speech recognition (AVSR) applications. The performance of the proposed recognition system is evaluated on three publicly available audio-visual datasets, using the generative Hidden Markov Model, and three discriminative techniques, viz. random forests, support vector machines, and adaptive boosting. In Chapter 5, Das and Roy provide in-depth concepts of various Deep Learning techniques for spoken language identification, including their advantages and limitations. In Chapter 6, Jat et al. suggest a conceptual system design to enable people to automate processes in the home by using voice commands. In Chapter 7, NithyaKalyani and Jothilakshmi discuss several approaches for extractive and abstractive speech summarization, and they investigate speech summarization in the Indian language. Additionally, the chapter analyzes various speech recognition techniques and their performance on recognizing Tamil speech data. In Chapter 8, Sarkar and Dey introduce the dynamics of emotional speech signals using recurrence analysis. In Chapter 9, Karan et al. introduced nonconventional techniques for speech processing that overcame the problem of short-time processing of the speech signal. In Chapter 10, Saha et al. discuss the artificially intelligent customized voice response system design using speech synthesis markup language. This chapter introduces a low-cost artificially intelligent voice response system driven by the Amazon Web Server on an IoT cloud platform and Raspberry Pi.

This book supports and enhances the utilization of speech analytics in several systems and real-world activities. It provides a well-standing forum to discuss the characteristics of the intelligent speech signal processing systems in different domains. The book is proposed for professionals, scientists, and engineers who are involved in the new techniques of intelligent speech signal processing methods and systems.