14 citations · 45 across the 8 of their papers we have counts for
10 papers
Federated Self-Supervised Learning for Acoustic Event Classification
Meng Feng, Chieh-Chi Kao, Qingming Tang +4
Standard acoustic event classification (AEC) solutions require large-scale collection of data from client devices for model optimization. Federated learning (FL) is a compelling fr…
Neural model robustness for skill routing in large-scale conversational AI systems: A design choice exploration
Han Li, Sunghyun Park, Aswarth Dara +5
Current state-of-the-art large-scale conversational AI or intelligent digital assistant systems in industry comprises a set of components such as Automatic Speech Recognition (ASR)…
Contrastive Unsupervised Learning for Speech Emotion Recognition
Mao Li, Bo Yang, Joshua Levy +6
Speech emotion recognition (SER) is a key technology to enable more natural human-machine communication. However, SER has long suffered from a lack of public large-scale labeled da…
Joint Turn and Dialogue level User Satisfaction Estimation on Multi-Domain Conversations
Praveen Kumar Bodigutla, Aditya Tiwari, Josep Valls Vargas +2
Dialogue level quality estimation is vital for optimizing data driven dialogue management. Current automated methods to estimate turn and dialogue level user satisfaction employ ha…
Data Augmentation for Training Dialog Models Robust to Speech Recognition Errors
Longshaokan Wang, Maryam Fazel-Zarandi, Aditya Tiwari +2
Speech-based virtual assistants, such as Amazon Alexa, Google assistant, and Apple Siri, typically convert users' audio signals to text data through automatic speech recognition (A…
Few-shot acoustic event detection via meta-learning
Bowen Shi, Ming Sun, Krishna C. Puvvada +3
We study few-shot acoustic event detection (AED) in this paper. Few-shot learning enables detection of new events with very limited labeled data. Compared to other research areas l…