Search Results for author:
Found 14 papers, 2 papers with code
SmarTerp: A CAI System to Support Simultaneous Interpreters in Real-Time
We present a system to support simultaneous interpreting in specific domains.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Cross-corpora experiments of automatic proficiency assessment and error detection for spoken English
The growing demand for learning English as a second language has led to an increasing interest in automatic approaches for assessing spoken language proficiency.
MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU Languages
The rise of foundation models (FMs), coupled with regulatory efforts addressing their risks and impacts, has sparked significant interest in open-source models.
L2 proficiency assessment using self-supervised speech representations
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.
Proficiency assessment of L2 spoken English using wav2vec 2.0
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
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.
Mixtures of Deep Neural Experts for Automated Speech Scoring
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
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.
Learning to Rank Microphones for Distant Speech Recognition
Fully exploiting ad-hoc microphone networks for distant speech recognition is still an open issue.
Automatically Assess Children's Reading Skills
Our system, based on ASR technology, implements the Cornoldi{'}s MT battery, which is a well-known Italian test to assess reading skills.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
TLT-school: a Corpus of Non Native Children Speech
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
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.
Automatic Quality Estimation for ASR System Combination
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
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.
