881 research outputs found
Modeling Topic and Role Information in Meetings using the Hierarchical Dirichlet Process
Abstract. In this paper, we address the modeling of topic and role information in multiparty meetings, via a nonparametric Bayesian model called the hierarchical Dirichlet process. This model provides a powerful solution to topic modeling and a flexible framework for the incorporation of other cues such as speaker role information. We present our modeling framework for topic and role on the AMI Meeting Corpus, and illustrate the effectiveness of the approach in the context of adapting a baseline language model in a large-vocabulary automatic speech recognition system for multiparty meetings. The adapted LM produces significant improvements in terms of both perplexity and word error rate.
Confidence Score Based Speaker Adaptation of Conformer Speech Recognition Systems
Speaker adaptation techniques provide a powerful solution to customise
automatic speech recognition (ASR) systems for individual users. Practical
application of unsupervised model-based speaker adaptation techniques to data
intensive end-to-end ASR systems is hindered by the scarcity of speaker-level
data and performance sensitivity to transcription errors. To address these
issues, a set of compact and data efficient speaker-dependent (SD) parameter
representations are used to facilitate both speaker adaptive training and
test-time unsupervised speaker adaptation of state-of-the-art Conformer ASR
systems. The sensitivity to supervision quality is reduced using a confidence
score-based selection of the less erroneous subset of speaker-level adaptation
data. Two lightweight confidence score estimation modules are proposed to
produce more reliable confidence scores. The data sparsity issue, which is
exacerbated by data selection, is addressed by modelling the SD parameter
uncertainty using Bayesian learning. Experiments on the benchmark 300-hour
Switchboard and the 233-hour AMI datasets suggest that the proposed confidence
score-based adaptation schemes consistently outperformed the baseline
speaker-independent (SI) Conformer model and conventional non-Bayesian, point
estimate-based adaptation using no speaker data selection. Similar consistent
performance improvements were retained after external Transformer and LSTM
language model rescoring. In particular, on the 300-hour Switchboard corpus,
statistically significant WER reductions of 1.0%, 1.3%, and 1.4% absolute
(9.5%, 10.9%, and 11.3% relative) were obtained over the baseline SI Conformer
on the NIST Hub5'00, RT02, and RT03 evaluation sets respectively. Similar WER
reductions of 2.7% and 3.3% absolute (8.9% and 10.2% relative) were also
obtained on the AMI development and evaluation sets.Comment: IEEE/ACM Transactions on Audio, Speech, and Language Processin
AGI for Agriculture
Artificial General Intelligence (AGI) is poised to revolutionize a variety of
sectors, including healthcare, finance, transportation, and education. Within
healthcare, AGI is being utilized to analyze clinical medical notes, recognize
patterns in patient data, and aid in patient management. Agriculture is another
critical sector that impacts the lives of individuals worldwide. It serves as a
foundation for providing food, fiber, and fuel, yet faces several challenges,
such as climate change, soil degradation, water scarcity, and food security.
AGI has the potential to tackle these issues by enhancing crop yields, reducing
waste, and promoting sustainable farming practices. It can also help farmers
make informed decisions by leveraging real-time data, leading to more efficient
and effective farm management. This paper delves into the potential future
applications of AGI in agriculture, such as agriculture image processing,
natural language processing (NLP), robotics, knowledge graphs, and
infrastructure, and their impact on precision livestock and precision crops. By
leveraging the power of AGI, these emerging technologies can provide farmers
with actionable insights, allowing for optimized decision-making and increased
productivity. The transformative potential of AGI in agriculture is vast, and
this paper aims to highlight its potential to revolutionize the industry
Concept Type Prediction and Responsive Adaptation in a Dialogue System
Responsive adaptation in spoken dialog systems involves a change in dialog system behavior in response to a user or a dialog situation. In this paper we address responsive adaptation in the automatic speech recognition (ASR) module of a spoken dialog system. We hypothesize that information about the content of a user utterance may help improve speech recognition for the utterance. We use a two-step process to test this hypothesis: first, we automatically predict the task-relevant concept types likely to be present in a user utterance using features from the dialog context and from the output of first-pass ASR of the utterance; and then, we adapt the ASR's language model to the predicted content of the user's utterance and run a second pass of ASR. We show that: (1) it is possible to achieve high accuracy in determining presence or absence of particular concept types in a post-confirmation utterance; and (2) 2-pass speech recognition with concept type classification and language model adaptation can lead to improved speech recognition performance for post-confirmation utterances
SwissBERT: The Multilingual Language Model for Switzerland
We present SwissBERT, a masked language model created specifically for processing Switzerland-related text. SwissBERT is a pre-trained model that we adapted to news articles written in the national languages of Switzerland -- German, French, Italian, and Romansh. We evaluate SwissBERT on natural language understanding tasks related to Switzerland and find that it tends to outperform previous models on these tasks, especially when processing contemporary news and/or Romansh Grischun. Since SwissBERT uses language adapters, it may be extended to Swiss German dialects in future work. The model and our open-source code are publicly released at https://github.com/ZurichNLP/swissbert
Surgicberta: a pre-trained language model for procedural surgical language
Pre-trained language models are now ubiquitous in natural language processing, being successfully applied for many different tasks and in several real-world applications. However, even though there is a wealth of high-quality written materials on surgery, and the scientific community has shown a growing interest in the application of natural language processing techniques in surgery, a pre-trained language model specific to the surgical domain is still missing. The creation and public release of such a model would serve numerous useful clinical applications. For example, it could enhance existing surgical knowledge bases employed for task automation, or assist medical students in summarizing complex surgical descriptions. For this reason, in this paper, we introduce SurgicBERTa, a pre-trained language model specific for the English surgical language, i.e., the language used in the surgical domain. SurgicBERTa has been obtained from RoBERTa through continued pre-training with the Masked language modeling objective on 300 k sentences taken from English surgical books and papers, for a total of 7 million words. By publicly releasing SurgicBERTa, we make available a resource built from the content collected in many high-quality surgical books, online textual resources, and academic papers. We performed several assessments in order to evaluate SurgicBERTa, comparing it with the general domain RoBERTa. First, we intrinsically assessed the model in terms of perplexity, accuracy, and evaluation loss resulting from the continual training according to the masked language modeling task. Then, we extrinsically evaluated SurgicBERTa on several downstream tasks, namely (i) procedural sentence detection, (ii) procedural knowledge extraction, (iii) ontological information discovery, and (iv) surgical terminology acquisition. Finally, we conducted some qualitative analysis on SurgicBERTa, showing that it contains a lot of surgical knowledge that could be useful to enrich existing state-of-the-art surgical knowledge bases or to extract surgical knowledge. All the assessments show that SurgicBERTa better deals with surgical language than a general-purpose pre-trained language model such as RoBERTa, and therefore can be effectively exploited in many computer-assisted applications in the surgical domain
Smooth inverse frequency based text data selection for medical dictation
Under-resourced domain problem is significant in automatic speech recognition, especially in small languages such as Hungarian or in fields where data is often confidential such as finance and medicine. We introduce a method using word embedding and smooth inverse frequency (SIF) based distance measurement to filter public domain web corpora. The selection for (medical) domain matching documents can be scaled. The resulted text is used to train an augmented language model for a medical dictation system. We show that using the appropriately scaled selection leads to optimal performance of the ASR system over the baselines where no data augmentation was applied or all the augmentation data was added
- âŠ