activity
20172021
most citedTLT-school: a Corpus of Non Native Children Speech

13 citations · 25 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CL2021

Seed Words Based Data Selection for Language Model Adaptation

Roberto Gretter, Marco Matassoni, Daniele Falavigna

We address the problem of language model customization in applications where the ASR component needs to manage domain-specific terminology; although current state-of-the-art speech…

cs.CL20215 cited

Experiments of ASR-based mispronunciation detection for children and adult English learners

Nina Hosseini-Kivanani, Roberto Gretter, Marco Matassoni +1

Pronunciation is one of the fundamentals of language learning, and it is considered a primary factor of spoken language when it comes to an understanding and being understood by ot…

eess.AS2021

Learning to Rank Microphones for Distant Speech Recognition

Samuele Cornell, Alessio Brutti, Marco Matassoni +1

Fully exploiting ad-hoc microphone networks for distant speech recognition is still an open issue. Empirical evidence shows that being able to select the best microphone leads to s…

cs.CL202013 cited

TLT-school: a Corpus of Non Native Children Speech

Roberto Gretter, Marco Matassoni, Stefano Bannò +1

This paper describes "TLT-school" a corpus of speech utterances collected in schools of northern Italy for assessing the performance of students learning both English and German. T…

cs.CL2018

Non-native children speech recognition through transfer learning

Marco Matassoni, Roberto Gretter, Daniele Falavigna +1

This work deals with non-native children's speech and investigates both multi-task and transfer learning approaches to adapt a multi-language Deep Neural Network (DNN) to speakers,…

cs.CL20177 cited

Automatic Quality Estimation for ASR System Combination

Shahab Jalalvand, Matteo Negri, Daniele Falavigna +2

Recognizer Output Voting Error Reduction (ROVER) has been widely used for system combination in automatic speech recognition (ASR). In order to select the most appropriate words to…