The forced alignment technique developed in this work can be generalized and applied to create speech datasets for other low-resource languages by adapting its core principles to the specific linguistic characteristics and phonetic structures of those languages. The first step involves developing a robust transcription module that can handle the unique phonemes and intonational patterns of the target language. This may require training or fine-tuning existing ASR models on language-specific datasets to improve their accuracy.
Next, the alignment process can be tailored to accommodate the linguistic features of the new language. For instance, the start-end alignment and forced alignment methods can be adjusted to account for variations in speech patterns, such as the presence of compound words or language-specific syntactic structures. Utilizing a combination of interval and gapped search methods, as demonstrated in the ManaTTS project, can enhance the flexibility of the alignment process, allowing it to handle discrepancies between spoken and written forms effectively.
Furthermore, collaboration with native speakers and linguists can provide valuable insights into the nuances of the language, ensuring that the alignment process is culturally and contextually appropriate. This collaboration can also facilitate the collection of high-quality audio samples that reflect the diversity of speakers within the language community.
Finally, the open-source nature of the tools and methodologies developed in this work allows for easy adaptation and sharing among researchers working on low-resource languages. By providing clear documentation and guidelines, the forced alignment technique can be disseminated widely, encouraging its application in various linguistic contexts and contributing to the development of high-quality speech datasets across different languages.