Speech Technology Progress Based on New Machine Learning Paradigm

Area of Science:

• Speech technology, signal processing, machine learning, computational intelligence, cognitive science, and interdisciplinary research.

Background:

• Speech technologies have evolved significantly over decades, with recent breakthroughs driven by machine learning paradigms.
• The complexity and cognitive science links of speech make it an interdisciplinary field.
• Progress in speech signal processing is intrinsically tied to advancements in machine learning.

Purpose of the Study:

• To provide an overview of speech signal analysis and processing.
• To detail machine learning algorithms and computational intelligence applied to speech.
• To cover key areas including speech production, perception, recognition, synthesis, and dialogue systems.

Main Methods:

• Review of speech signal processing techniques.
• Analysis of machine learning and computational intelligence algorithms.
• Exploration of cognitive aspects in speech communication and language understanding.
• Examination of speech signal compression, coding, and transmission.

Main Results:

• Significant progress in speech recognition and text-to-speech synthesis due to machine learning.
• Advancements in spoken dialogue systems development.
• New insights into speech signal compression and cognitive speech coding.
• Identification of recent achievements and ongoing challenges in the field.

Conclusions:

• Machine learning has profoundly impacted speech signal processing in the last decade.
• Interdisciplinary approaches are crucial for understanding and advancing speech technologies.
• Future research should focus on leveraging machine learning for further innovation in speech communication.