14 citations · 52 across the 11 of their papers we have counts for
11 papers
Toward More Accurate and Generalizable Evaluation Metrics for Task-Oriented Dialogs
Abishek Komma, Nagesh Panyam Chandrasekarasastry, Timothy Leffel +4
Measurement of interaction quality is a critical task for the improvement of spoken dialog systems. Existing approaches to dialog quality estimation either focus on evaluating the…
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…
Multi-domain Conversation Quality Evaluation via User Satisfaction Estimation
Praveen Kumar Bodigutla, Lazaros Polymenakos, Spyros Matsoukas
An automated metric to evaluate dialogue quality is vital for optimizing data driven dialogue management. The common approach of relying on explicit user feedback during a conversa…
Investigation of Error Simulation Techniques for Learning Dialog Policies for Conversational Error Recovery
Maryam Fazel-Zarandi, Longshaokan Wang, Aditya Tiwari +1
Training dialog policies for speech-based virtual assistants requires a plethora of conversational data. The data collection phase is often expensive and time consuming due to huma…
Domain-Independent turn-level Dialogue Quality Evaluation via User Satisfaction Estimation
Praveen Kumar Bodigutla, Longshaokan Wang, Kate Ridgeway +4
An automated metric to evaluate dialogue quality is vital for optimizing data driven dialogue management. The common approach of relying on explicit user feedback during a conversa…
Compression of Acoustic Event Detection Models With Quantized Distillation
Bowen Shi, Ming Sun, Chieh-Chi Kao +3
Acoustic Event Detection (AED), aiming at detecting categories of events based on audio signals, has found application in many intelligent systems. Recently deep neural network sig…