Search Results for author: Marco Matassoni

Found 13 papers, 1 papers with code

Cross-corpora experiments of automatic proficiency assessment and error detection for spoken English

no code implementations NAACL (BEA) 2022 Stefano Bannò, Marco Matassoni

The growing demand for learning English as a second language has led to an increasing interest in automatic approaches for assessing spoken language proficiency.

L2 proficiency assessment using self-supervised speech representations

no code implementations16 Nov 2022 Stefano Bannò, Kate M. Knill, Marco Matassoni, Vyas Raina, Mark J. F. Gales

Though the wav2vec 2. 0 based system is found to be sensitive to the nature of the response, it can be configured to yield comparable performance to systems requiring a speech transcription, and yields gains when appropriately combined with standard approaches.

speech-recognition Speech Recognition

Proficiency assessment of L2 spoken English using wav2vec 2.0

no code implementations24 Oct 2022 Stefano Bannò, Marco Matassoni

The increasing demand for learning English as a second language has led to a growing interest in methods for automatically assessing spoken language proficiency.

Seed Words Based Data Selection for Language Model Adaptation

no code implementations MTSummit 2021 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 recognition technology provides excellent results for generic domains, the adaptation to specialized dictionaries or glossaries is still an open issue.

Language Modelling Semantic Similarity +3

Mixtures of Deep Neural Experts for Automated Speech Scoring

no code implementations23 Jun 2021 Sara Papi, Edmondo Trentin, Roberto Gretter, Marco Matassoni, Daniele Falavigna

The paper copes with the task of automatic assessment of second language proficiency from the language learners' spoken responses to test prompts.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

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

no code implementations13 Apr 2021 Nina Hosseini-Kivanani, Roberto Gretter, Marco Matassoni, Giuseppe Daniele Falavigna

In this study, we develop a mispronunciation assessment system that checks the pronunciation of non-native English speakers, identifies the commonly mispronounced phonemes of Italian learners of English, and presents an evaluation of the non-native pronunciation observed in phonetically annotated speech corpora.

Language Modelling speech-recognition +1

TLT-school: a Corpus of Non Native Children Speech

no code implementations LREC 2020 Roberto Gretter, Marco Matassoni, Stefano Bannò, Daniele Falavigna

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.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Non-native children speech recognition through transfer learning

no code implementations25 Sep 2018 Marco Matassoni, Roberto Gretter, Daniele Falavigna, Diego Giuliani

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, specifically children, learning a foreign language.

speech-recognition Speech Recognition +1

Automatic Quality Estimation for ASR System Combination

no code implementations22 Jun 2017 Shahab Jalalvand, Matteo Negri, Daniele Falavigna, Marco Matassoni, Marco Turchi

In this paper we propose a novel variant of ROVER that takes advantage of ASR quality estimation (QE) for ranking the transcriptions at "segment level" instead of: i) relying on confidence scores, or ii) feeding ROVER with randomly ordered hypotheses.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

DNN adaptation by automatic quality estimation of ASR hypotheses

no code implementations6 Feb 2017 Daniele Falavigna, Marco Matassoni, Shahab Jalalvand, Matteo Negri, Marco Turchi

Our hypothesis is that significant improvements can be achieved by: i)automatically transcribing the evaluation data we are currently trying to recognise, and ii) selecting from it a subset of "good quality" instances based on the word error rate (WER) scores predicted by a QE component.

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