Speech Analysis
Speech analysis is a rapidly evolving field focused on understanding and manipulating spoken language using computational methods, aiming to improve human-computer interaction and address challenges in healthcare and other domains. Current research emphasizes developing robust models, often based on transformer networks and neural codecs, for tasks such as speech recognition, emotion detection, and generation, including handling multi-speaker scenarios and low-resource languages. These advancements have significant implications for applications ranging from improved accessibility for individuals with speech impairments to more natural and intuitive interfaces for various technologies, as well as enabling new diagnostic tools in healthcare.
Papers
Exploiting Phonological Similarities between African Languages to achieve Speech to Speech Translation
Peter Ochieng, Dennis Kaburu
Lina-Speech: Gated Linear Attention is a Fast and Parameter-Efficient Learner for text-to-speech synthesis
Théodor Lemerle, Harrison Vanderbyl, Vaibhav Srivastav, Nicolas Obin, Axel Roebel
Robust and Explainable Depression Identification from Speech Using Vowel-Based Ensemble Learning Approaches
Kexin Feng, Theodora Chaspari
VoiceTextBlender: Augmenting Large Language Models with Speech Capabilities via Single-Stage Joint Speech-Text Supervised Fine-Tuning
Yifan Peng, Krishna C. Puvvada, Zhehuai Chen, Piotr Zelasko, He Huang, Kunal Dhawan, Ke Hu, Shinji Watanabe, Jagadeesh Balam, Boris Ginsburg
Continuous Speech Tokenizer in Text To Speech
Yixing Li, Ruobing Xie, Xingwu Sun, Yu Cheng, Zhanhui Kang
Can a Machine Distinguish High and Low Amount of Social Creak in Speech?
Anne-Maria Laukkanen, Sudarsana Reddy Kadiri, Shrikanth Narayanan, Paavo Alku
Chatting with Bots: AI, Speech Acts, and the Edge of Assertion
Iwan Williams, Tim Bayne