Investigating the Effect of ASR Tuning on Named Entity Recognition

Jannet, Mohamed Ameur Ben; Galibert, Olivier; Adda-Decker, Martine; Rosset, Sophie

doi:10.21437/interspeech.2017-1482

Cited by 5 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, this metric does not distinguish between errors on verbs or proper nouns while such errors do not have the same impact for NER. To compensate this problem, some dedicated metrics to tune ASR systems for better NER performances have been proposed, such as in [3]. Another inconvenience is that usually no information about named entities are used in the ASR process, while such information could help to better choose the partial recognition hypotheses that are dropped away during the decoding process.…”

Section: Introductionmentioning

confidence: 99%

End-To-End Named Entity And Semantic Concept Extraction From Speech

Ghannay

Caubrière

Estève

et al. 2018

2018 IEEE Spoken Language Technology Workshop (SLT)

View full text Add to dashboard Cite

Named entity recognition (NER) is among SLU tasks that usually extract semantic information from textual documents. Until now, NER from speech is made through a pipeline process that consists in processing first an automatic speech recognition (ASR) on the audio and then processing a NER on the ASR outputs. Such approach has some disadvantages (error propagation, metric to tune ASR systems sub-optimal in regards to the final task, reduced space search at the ASR output level,...) and it is known that more integrated approaches outperform sequential ones, when they can be applied. In this paper, we present a first study of end-to-end approach that directly extracts named entities from speech, though a unique neural architecture. On a such way, a joint optimization is able for both ASR and NER. Experiments are carried on French data easily accessible, composed of data distributed in several evaluation campaign. Experimental results show that this end-to-end approach provides better results (F-measure=0.69 on test data) than a classical pipeline approach to detect named entity categories (F-measure=0.65).

show abstract

Section: Introductionmentioning

confidence: 99%

End-To-End Named Entity And Semantic Concept Extraction From Speech

Ghannay

Caubrière

Estève

et al. 2018

2018 IEEE Spoken Language Technology Workshop (SLT)

View full text Add to dashboard Cite

show abstract

“…Another group of similar tasks are information retrieval from speech [16,17,18,19]. These tasks are either based on a pipeline of several systems or a single E2E system [18].…”

Section: Related Workmentioning

confidence: 99%

Approaches to Improving Recognition of Underrepresented Named Entities in Hybrid ASR Systems

Mao

Khassanov

Pham

et al. 2020

Preprint

View full text Add to dashboard Cite

In this paper, we present a series of complementary approaches to improve the recognition of underrepresented named entities (NE) in hybrid ASR systems without compromising overall word error rate performance. The underrepresented words correspond to rare or out-of-vocabulary (OOV) words in the training data, and thereby can't be modeled reliably. We begin with graphemic lexicon which allows to drop the necessity of phonetic models in hybrid ASR. We study it under different settings and demonstrate its effectiveness in dealing with underrepresented NEs. Next, we study the impact of neural language model (LM) with letter-based features derived to handle infrequent words. After that, we attempt to enrich representations of underrepresented NEs in pretrained neural LM by borrowing the embedding representations of rich-represented words. This let us gain significant performance improvement on underrepresented NE recognition. Finally, we boost the likelihood scores of utterances containing NEs in the word lattices rescored by neural LMs and gain further performance improvement. The combination of the aforementioned approaches improves NE recognition by up to 42% relatively.

show abstract

“…Additionally, ASR systems are generally tuned measuring word error rate (WER) on a validation corpus, but this metric is not optimal for the subsequent SLU task (semantic parsing, NER, etc.). To compensate this, some specialized metrics to tune ASR have been proposed [2]. However, the number of SLU task applied on the ASR output may be large and considering dedicated metrics for each one of them is not feasible.…”

Section: Introductionmentioning

confidence: 99%

Adapting a FrameNet Semantic Parser for Spoken Language Understanding Using Adversarial Learning

Marzinotto¹,

Damnati²,

Béchet³

2019

Interspeech 2019

View full text Add to dashboard Cite

This paper presents a new semantic frame parsing model, based on Berkeley FrameNet, adapted to process spoken documents in order to perform information extraction from broadcast contents. Building upon previous work that had shown the effectiveness of adversarial learning for domain generalization in the context of semantic parsing of encyclopedic written documents, we propose to extend this approach to elocutionary style generalization. The underlying question throughout this study is whether adversarial learning can be used to combine data from different sources and train models on a higher level of abstraction in order to increase their robustness to lexical and stylistic variations as well as automatic speech recognition errors. The proposed strategy is evaluated on a French corpus of encyclopedic written documents and a smaller corpus of radio podcast transcriptions, both annotated with a FrameNet paradigm. We show that adversarial learning increases all models generalization capabilities both on manual and automatic speech transcription as well as on encyclopedic data.

show abstract

Investigating the Effect of ASR Tuning on Named Entity Recognition

Cited by 5 publications

References 9 publications

End-To-End Named Entity And Semantic Concept Extraction From Speech

End-To-End Named Entity And Semantic Concept Extraction From Speech

Approaches to Improving Recognition of Underrepresented Named Entities in Hybrid ASR Systems

Adapting a FrameNet Semantic Parser for Spoken Language Understanding Using Adversarial Learning

Contact Info

Product

Resources

About