Sökning: "Speech Learning Model"

Visar resultat 11 - 15 av 100 uppsatser innehållade orden Speech Learning Model.

1. 11. Improving accuracy of speech recognition for low resource accents : Testing the performance of fine-tuned Wav2vec2 models on accented Swedish

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Arash Dabiri; [2023]
   Nyckelord :Speech-to-text; deep learning; accents; wav2vec; tal-till-text; djupinlärning; brytningar; wav2vec;

   Sammanfattning : While the field of speech recognition has recently advanced quickly, even the highest performing models struggle with accents. There are several methods of improving the performance on accents, but many are hard to implement or need high amounts of data and are therefore costly to implement. LÄS MER

3. 12. A Hybrid Approach to Hate Speech Detection

   Master-uppsats, Umeå universitet/Institutionen för datavetenskap

   Författare :Simon Rickardsson; [2023]
   Nyckelord :;

   Sammanfattning : An interesting question is to what extent can background knowledge help in the context of text classification. To address this in more detail, can a traditional rulebased classifier help boost the accuracy of learned models? We explore this here for detecting hate speech and offensive language in online text. LÄS MER

4. 13. Cross-Lingual and Genre-Supervised Parsing and Tagging for Low-Resource Spoken Data

   Master-uppsats, Uppsala universitet/Institutionen för lingvistik och filologi

   Författare :Iliana Fosteri; [2023]
   Nyckelord :dependency parsing; part-of-speech tagging; low-resource languages; transcribed speech; large language models; cross-lingual learning; transfer learning; multi-task learning; Universal Dependencies;

   Sammanfattning : Dealing with low-resource languages is a challenging task, because of the absence of sufficient data to train machine-learning models to make predictions on these languages. One way to deal with this problem is to use data from higher-resource languages, which enables the transfer of learning from these languages to the low-resource target ones. LÄS MER

5. 14. Multi-Label Toxic Comment Classification Using Machine Learning: An In-Depth Study

   Master-uppsats, Lunds universitet/Institutionen för datavetenskap

   Författare :Matilda Froste; Mosa Hosseini; [2023]
   Nyckelord :natural language processing; machine learning; offensive speech detection; transformers; multi-label classification; Technology and Engineering;

   Sammanfattning : The classification of toxic comments is a well-researched area with many techniques available. However, effectively managing multi-label categorization still requires a considerable amount of work. LÄS MER

7. 15. Exploring toxic lexicon similarity methods with the DRG framework on the toxic style transfer task

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Martin Iglesias; [2023]
   Nyckelord :Detoxifcation; Text style transfer; Deep learning; Transformers; Linguistics; Natural Language Processing; Hate speech; Text style-conditional generation; Large language model; Avgiftning; Överföring av textstil; Djupinlärning; Transformatorer; Lingvistik; Naturlig språkbehandling; Hatprat; Textstil - villkorlig generering; Stor språkmodell;

   Sammanfattning : The topic of this thesis is the detoxification of language in social networks with a particular focus on style transfer techniques that combine deep learning and linguistic resources. In today’s digital landscape, social networks are rife with communication that can often be toxic, either intentionally or unintentionally. LÄS MER