Search Results for author:
Found 19 papers, 5 papers with code
Large Language Models Are Strong Audio-Visual Speech Recognition Learners
For example, in the audio and speech domains, an LLM can be equipped with (automatic) speech recognition (ASR) abilities by just concatenating the audio tokens, computed with an audio encoder, and the text tokens to achieve state-of-the-art results.
Audio-Visual Speech Recognition
Automatic Speech Recognition
+3
Federating Dynamic Models using Early-Exit Architectures for Automatic Speech Recognition on Heterogeneous Clients
Automatic speech recognition models require large amounts of speech recordings for training.
Efficient Fine-tuning of Audio Spectrogram Transformers via Soft Mixture of Adapters
It exploits adapters as the experts and, leveraging the recent Soft MoE method, it relies on a soft assignment between the input tokens and experts to keep the computational time limited.
Parameter-Efficient Transfer Learning of Audio Spectrogram Transformers
Parameter-efficient transfer learning (PETL) methods have emerged as a solid alternative to the standard full fine-tuning approach.
Continual Contrastive Spoken Language Understanding
In this paper, we investigate the problem of learning sequence-to-sequence models for spoken language understanding in a class-incremental learning (CIL) setting and we propose COCONUT, a CIL method that relies on the combination of experience replay and contrastive learning.
Training dynamic models using early exits for automatic speech recognition on resource-constrained devices
In self-attention models for automatic speech recognition (ASR), early-exit architectures enable the development of dynamic models capable of adapting their size and architecture to varying levels of computational resources and ASR performance demands.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Sequence-Level Knowledge Distillation for Class-Incremental End-to-End Spoken Language Understanding
The ability to learn new concepts sequentially is a major weakness for modern neural networks, which hinders their use in non-stationary environments.
Improving the Intent Classification accuracy in Noisy Environment
Intent classification is a fundamental task in the spoken language understanding field that has recently gained the attention of the scientific community, mainly because of the feasibility of approaching it with end-to-end neural models.
Scaling strategies for on-device low-complexity source separation with Conv-Tasnet
Given the modular nature of the well-known Conv-Tasnet speech separation architecture, in this paper we consider three parameters that directly control the overall size of the model, namely: the number of residual blocks, the number of repetitions of the separation blocks and the number of channels in the depth-wise convolutions, and experimentally evaluate how they affect the speech separation performance.
An Investigation of the Combination of Rehearsal and Knowledge Distillation in Continual Learning for Spoken Language Understanding
Continual learning refers to a dynamical framework in which a model receives a stream of non-stationary data over time and must adapt to new data while preserving previously acquired knowledge.
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
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
Automatic assessment of spoken language proficiency of non-native children
This paper describes technology developed to automatically grade Italian students (ages 9-16) on their English and German spoken language proficiency.
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.
