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
Modality Dropout for Multimodal Device Directed Speech Detection using Verbal and Non-Verbal Features
Gautam Krishna, Sameer Dharur, Oggi Rudovic, Pranay Dighe, Saurabh Adya, Ahmed Hussen Abdelaziz, Ahmed H Tewfik
DPP-TTS: Diversifying prosodic features of speech via determinantal point processes
Seongho Joo, Hyukhun Koh, Kyomin Jung
SPRING-INX: A Multilingual Indian Language Speech Corpus by SPRING Lab, IIT Madras
Nithya R, Malavika S, Jordan F, Arjun Gangwar, Metilda N J, S Umesh, Rithik Sarab, Akhilesh Kumar Dubey, Govind Divakaran, Samudra Vijaya K, Suryakanth V Gangashetty