Introduction to Awesome-Speech-Recognition-Speech-Synthesis-Papers
The "awesome-speech-recognition-speech-synthesis-papers" project provides an extensive compilation of research papers related to various aspects of speech technology. This curated list serves as an invaluable resource for students, researchers, and professionals interested in the field of speech recognition and synthesis. The repository is structured into several key categories, each addressing specific areas of study within the domain.
Paper List Overview
Text-to-Audio
This section explores the cutting-edge field of converting textual input into audio output. The papers discuss innovative methods such as AudioLM and AudioLDM that leverage language models and latent diffusion techniques to effectively generate audio content from text. Other notable works like MusicLM and Moûsai focus on the generation of music from text, showcasing the intersection of language and creative expression.
Automatic Speech Recognition (ASR)
Automatic Speech Recognition is a pivotal component of speech technology that involves converting spoken language into text. This section presents foundational research and advancements over the years, highlighting key technologies like Hidden Markov Models (HMM), neural network-based approaches such as Convolutional Neural Networks (CNN), and innovations like deep learning and recurrent neural networks that have significantly enhanced speech recognition capabilities.
Speaker Verification
Research in this category emphasizes techniques for verifying the identity of a speaker based on voice characteristics. The included papers address various methodologies and improvements in the field, instrumental for applications in security and personalized user experiences.
Voice Conversion (VC)
Voice Conversion involves transforming the voice of one speaker to sound like that of another without altering the linguistic content. The papers in this section explore the technological strides made in achieving seamless and realistic voice transformations.
Speech Synthesis (TTS)
Speech Synthesis, or Text-to-Speech, is the process of converting written text into spoken words. The compiled papers delve into different techniques, including advancements in neural network architectures and machine learning frameworks, that contribute to more natural and human-like synthesized speech.
Language Modelling
This category presents research focused on designing models that can predict or generate human language. Language models play a crucial role in understanding and producing speech, forming the backbone of many advanced speech processing applications.
Confidence Estimates
Confidence estimation is essential for assessing the reliability of speech recognition results. The papers here examine methods for calculating the confidence levels in speech recognition outputs, aiding in the development of more robust systems that can handle errors and uncertainties effectively.
Music Modelling
By extending beyond traditional speech, this section includes research on modeling musical elements and generating music. Papers like Noise2Music discuss innovative approaches using diffusion models to create music conditioned on text inputs, pushing the boundaries of what's possible in audio generation.
Interesting Papers
Lastly, the repository also features a selection of papers that stand out for their innovative contributions to the overall field of speech recognition and synthesis. These works offer unique perspectives and methodologies that enrich the understanding of speech technology.
Through this comprehensive repository, the awesome-speech-recognition-speech-synthesis-papers project offers a valuable platform for learning and inspiration, fostering further advancement in the dynamic and evolving field of speech technologies.