Automatic Speech Recognition

Our research in this area focus on recognizing and understanding user speech in languages with little data, such as code-mixing speech in Hindi-English or non-standard Chinese. We are also working on spoken language systems where the system answers user queries and learns over time from these interactions. A current focus is to render such systems "empathetic".

Natural Language Processing

We focus on statistical NLP problems such as bilingual lexicon extraction from large and small corpora, statistical semantic parsing, cross-lingual information retrieval and structured summarization. One of our research highlights is the joint optimization of speech and language models for different applications such as spoken language summarization, speech translation, and dialog systems.

Emotion, Mood and Sentiment Analysis

Our research interest in this area includes the recognition of emotion, mood and sentiment from mutli-channel inputs such as speech, language, facial experession and gesture. We also analyze music audio signals as well as music lyrics to enable efficient retrieval of millions and tens of millions of songs by genres, styles, mood, and artist. We are interested in applying emotion and sentiment analysis to social media, the financial domain and creative arts.