Automatic Speech Recognition (ASR) enables a wide variety of practical applications. However, many applications have their own domain-specific words, creating a gap between training and test data when used in practice. Domain adaptation can be achieved through model fine-tuning, but it requires domain-specific speech data paired with transcripts, which is labor intensive to produce. Fortunately, the dependence on audio data can be mitigated to a certain extent by incorporating text-based language models during decoding. This thesis explores approaches for creating domain-specific 4-gram models for a Swedish open-source wav2vec 2.0 model. The three main approaches extend a social media corpus with domain-specific data to estimate the models. The first approach utilizes a relatively small set of in-domain text data, and the second approach utilizes machine transcripts from another ASR system. Finally, the third approach utilizes Named Entity Recognition (NER) to find words of the same entity type in a corpus to replace with in-domain words. The 4-gram models are evaluated by the error rate (ERR) of recognizing in-domain words in a custom dataset. Additionally, the models are evaluated by the Word Error Rate (WER) on the Common Voice test set to ensure good overall performance. Compared to not having a language model, the base model improves the WER on Common Voice by 2.55 percentage points and the in-domain ERR by 6.11 percentage points. Next, adding in-domain text to the base model results in a 2.61 WER improvement and a 10.38 ERR improvement over not having a language model. Finally, adding in-domain machine transcripts and using the NER approach results in the same 10.38 ERR improvement as adding in-domain text but slightly less significant WER improvements of 2.56 and 2.47, respectively. These results contribute to the exploration of state-of-the-art Swedish ASR and have the potential to enable the adoption of open-source ASR models for more use cases. / Automatisk taligenkänning (ASR) möjliggör en mängd olika praktiska tillämpningar. Men många tillämpningsområden har sin egen uppsättning domänspecifika ord vilket kan skapa problem när en taligenkänningsmodell används på data som skiljer sig från träningsdatan. Taligenkänningsmodeller kan anpassas till nya domäner genom fortsatt träning med taldata, men det kräver tillgång till domänspecifik taldata med tillhörande transkript, vilket är arbetskrävande att producera. Lyckligtvis kan beroendet av ljuddata mildras till viss del genom användande av textbaserade språkmodeller tillsammans med taligenkänningsmodellerna. Detta examensarbete utforskar tillvägagångssätt för att skapa domänspecifika 4-gram-språkmodeller för en svensk wav2vec 2.0-modell som tränats av Kungliga Biblioteket. Utöver en basmodell så används tre huvudsakliga tillvägagångssätt för att utöka en korpus med domänspecifik data att träna modellerna från. Det första tillvägagångssättet använder en relativt liten mängd domänspecifik textdata, och det andra tillvägagångssättet använder transkript från ett annat ASR-system (maskintranskript). Slutligen använder det tredje tillvägagångssättet Named Entity Recognition (NER) för att hitta ord av samma entitetstyp i en korpus som sedan ersätts med domänspecifika ord. Språkmodellerna utvärderas med ett nytt domänspecifikt evalueringsdataset samt på testdelen av Common Voice datasetet. Jämfört med att inte ha en språkmodell förbättrar basmodellen Word Error Rate (WER) på Common Voice med 2,55 procentenheter och Error Rate (ERR) inom domänen med 6,11 procentenheter. Att lägga till domänspecifik text till basmodellens korpus resulterar i en 2,61 WER-förbättringochen10,38 ERR-förbättring jämfört med att inte ha en språkmodell. Slutligen, att lägga till domänspecifika maskintranskript och att använda NER-metoden resulterar i samma 10.38 ERR-förbättringar som att lägga till domänspecifik text men något mindre WER-förbättringar på 2.56 respektive 2.47 procentenheter. Den här studien bidrar till svensk ASR och kan möjliggöra användandet av öppna taligenkänningsmodeller för fler användningsområden.

In Automatic Speech Recognition, there is an extensive need for time-aligned data. Manual speech segmentation has been shown to be more laborious than manual transcription, especially when dealing with tens of hours of speech. Forced alignment is a technique for matching a signal with its orthographic transcription with respect to the duration of linguistic units. Most forced aligners, however, are language-dependent and trained on English data, whereas under-resourced languages lack the resources to develop an acoustic model required for an aligner, as well as manually aligned data. An alternative solution to the training of new models can be cross-language forced alignment, in which an aligner trained on one language is used for aligning data in another language.  This thesis aimed to evaluate state-of-the-art forced alignment algorithms available for Swedish and test whether a Swedish model could be applied for aligning Norwegian. Three approaches for forced aligners were employed: (1) one forced aligner based on Dynamic Time Warping and text-to-speech synthesis Aeneas, (2) two forced aligners based on Hidden Markov Models, namely the Munich AUtomatic Segmentation System (WebMAUS) and the Montreal Forced Aligner (MFA) and (3) Connectionist Temporal Classification (CTC) segmentation algorithm with two pre-trained and fine-tuned Wav2Vec2 Swedish models. First, small speech test sets for Norwegian and Swedish, covering different types of spontaneousness in the speech, were created and manually aligned to create gold-standard alignments. Second, the performance of the Swedish dataset was evaluated with respect to the gold standard. Finally, it was tested whether Swedish forced aligners could be applied for aligning Norwegian data. The performance of the aligners was assessed by measuring the difference between the boundaries set in the gold standard from that of the comparison alignment. The accuracy was estimated by calculating the proportion of alignments below a particular threshold proposed in the literature. It was found that the performance of the CTC segmentation algorithm with Wav2Vec2 (VoxRex) was superior to other forced alignment systems. The differences between the alignments of two Wav2Vec2 models suggest that the training data may have a larger influence on the alignments, than the architecture of the algorithm. In lower thresholds, the traditional HMM approach outperformed the deep learning models. Finally, findings from the thesis have demonstrated promising results for cross-language forced alignment using Swedish models to align related languages, such as Norwegian.