Using language models to improve a speech recognition based maritime emergency call detection system

Visa enkel post
dc.contributor.authorJohansson, Eric
dc.contributor.departmentChalmers tekniska högskola / Institutionen för matematiska vetenskapersv
dc.contributor.examinerAndersson, Adam
dc.contributor.supervisorRöshammar, Kristoffer
dc.contributor.supervisorZechner, Niklas
dc.date.accessioned2022-10-31T18:22:02Z
dc.date.available2022-10-31T18:22:02Z
dc.date.issued2022
dc.date.submitted2020
dc.description.abstractNovel applications of the transformers architechture as well as the availability of pre-trained models have drastically reduced the amount of data required to train successful speech-to-text (STT) models. By using the Connectionist Temporal Classification (CTC) algorithm, the process is further simplified as the training data does not have to be pre-segmented. This work aims to improve the performance of such a model developed to detect maritime VHF radio emergency calls by adding a language model to the CTC-decoding. We experiment with language models trained on several different text corpora and apply language models both in the decoding and on the resulting transcripts. The results indicate the importance of large amounts of domain-specific text. The results also show that a reduced Word Error Rate (WER) does not necessarily lead to an improvement in contextual comprehension. Finally, it is shown that relatively large improvements are given by fine-tuning various pre-trained STT-models on a curated dataset.
dc.identifier.coursecodeMVEX03
dc.identifier.urihttps://odr.chalmers.se/handle/20.500.12380/305783
dc.language.isoeng
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectSpeech to text, automatic speech recognition, natural language processing, NLP, language model, wav2vec2.0, VHF, emergency call detection
dc.titleUsing language models to improve a speech recognition based maritime emergency call detection system
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeComplex adaptive systems (MPCAS), MSc
Ladda ner
Original bundle
Visar 1 - 1 av 1
Bild (thumbnail)
Namn:
MasterThesis_Eric_Johansson_2022.pdf
Storlek:
2.01 MB
Format:
Adobe Portable Document Format
Beskrivning:
Ladda ner
License bundle
Visar 1 - 1 av 1
Bild (thumbnail)
Namn:
license.txt
Storlek:
1.64 KB
Format:
Item-specific license agreed upon to submission
Beskrivning:
Ladda ner
Samling
Examensarbeten för masterexamen