activity
20182020
most citedDeep neural networks for emotion recognition combining audio and transcripts

9 citations · 9 across the 1 of their papers we have counts for

collaborators

7 papers

cs.CL2020

WER we are and WER we think we are

Piotr Szymański, Piotr Żelasko, Mikolaj Morzy +6

Natural language processing of conversational speech requires the availability of high-quality transcripts. In this paper, we express our skepticism towards the recent reports of v…

cs.CL2020

Punctuation Prediction in Spontaneous Conversations: Can We Mitigate ASR Errors with Retrofitted Word Embeddings?

Łukasz Augustyniak, Piotr Szymanski, Mikołaj Morzy +5

Automatic Speech Recognition (ASR) systems introduce word errors, which often confuse punctuation prediction models, turning punctuation restoration into a challenging task. These…

eess.AS20199 cited

Deep neural networks for emotion recognition combining audio and transcripts

Jaejin Cho, Raghavendra Pappagari, Purva Kulkarni +3

In this paper, we propose to improve emotion recognition by combining acoustic information and conversation transcripts. On the one hand, an LSTM network was used to detect emotion…

cs.CL2019

Hierarchical Transformers for Long Document Classification

Raghavendra Pappagari, Piotr Żelasko, Jesús Villalba +2

BERT, which stands for Bidirectional Encoder Representations from Transformers, is a recently introduced language representation model based upon the transfer learning paradigm. We…

eess.AS2019

Avaya Conversational Intelligence: A Real-Time System for Spoken Language Understanding in Human-Human Call Center Conversations

Jan Mizgajski, Adrian Szymczak, Robert Głowski +14

Avaya Conversational Intelligence(ACI) is an end-to-end, cloud-based solution for real-time Spoken Language Understanding for call centers. It combines large vocabulary, real-time…

cs.CL2019

Towards Better Understanding of Spontaneous Conversations: Overcoming Automatic Speech Recognition Errors With Intent Recognition

Piotr Żelasko, Jan Mizgajski, Mikołaj Morzy +4

In this paper, we present a method for correcting automatic speech recognition (ASR) errors using a finite state transducer (FST) intent recognition framework. Intent recognition i…