activity
20182022
most citedDeep neural networks for emotion recognition combining audio and transcripts

9 citations · 10 across the 4 of their papers we have counts for

collaborators

9 papers

cs.CL20221 cited

MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation

Anna Currey, Maria Nădejde, Raghavendra Pappagari +5

As generic machine translation (MT) quality has improved, the need for targeted benchmarks that explore fine-grained aspects of quality has increased. In particular, gender accurac…

cs.CL2021

Beyond Isolated Utterances: Conversational Emotion Recognition

Raghavendra Pappagari, Piotr Żelasko, Jesús Villalba +2

Speech emotion recognition is the task of recognizing the speaker's emotional state given a recording of their utterance. While most of the current approaches focus on inferring em…

cs.CL2021

Joint prediction of truecasing and punctuation for conversational speech in low-resource scenarios

Raghavendra Pappagari, Piotr Żelasko, Agnieszka Mikołajczyk +2

Capitalization and punctuation are important cues for comprehending written texts and conversational transcripts. Yet, many ASR systems do not produce punctuated and case-formatted…

cs.CL2021

What Helps Transformers Recognize Conversational Structure? Importance of Context, Punctuation, and Labels in Dialog Act Recognition

Piotr Żelasko, Raghavendra Pappagari, Najim Dehak

Dialog acts can be interpreted as the atomic units of a conversation, more fine-grained than utterances, characterized by a specific communicative function. The ability to structur…

cs.SD2020

CopyPaste: An Augmentation Method for Speech Emotion Recognition

Raghavendra Pappagari, Jesús Villalba, Piotr Żelasko +2

Data augmentation is a widely used strategy for training robust machine learning models. It partially alleviates the problem of limited data for tasks like speech emotion recogniti…

eess.AS2020

x-vectors meet emotions: A study on dependencies between emotion and speaker recognition

Raghavendra Pappagari, Tianzi Wang, Jesus Villalba +2

In this work, we explore the dependencies between speaker recognition and emotion recognition. We first show that knowledge learned for speaker recognition can be reused for emotio…