How to develop effective Ukrainian speech-to-text modules

To develop effective Ukrainian speech-to-text (STT) modules, several key approaches and technologies can be leveraged:

• Use pretrained models as a base: Models like Whisper can be extended with task-specific modules and fine-tuned with limited labeled data to improve performance in low-resource languages such as Ukrainian. A novel fine-tuning approach improves performance and efficiency by removing irrelevant weights and reorganizing embeddings. ¹

• Collect and prepare high-quality training data: Creating datasets with Ukrainian conversational and podcast audio aligned with transcripts can provide much-needed training data for ASR models. For example, recent work released datasets with over 50 hours of text/audio pairs for Ukrainian. ²

• Employ hybrid deep learning architectures: Combining convolutional neural networks (CNN), ResNet, and other architectures in hybrid models tailored for speech-to-text can improve accuracy, as shown in healthcare telemedicine systems. ³

• Use transfer learning and cross-lingual knowledge transfer: Leveraging models trained on related languages or multilingual datasets, combined with iterative pseudo-labelling, can boost ASR accuracy in Ukrainian, especially for end-to-end neural transducer-based systems. ⁴

• Incorporate language-specific linguistic and syntactic features: Syntactic and morphological analysis tools adapted for Ukrainian can improve the linguistic understanding and recognition accuracy. ^{5, 6}

• Apply context-aware strategies: Using memory-augmented modules or document-level context integration improves recognition and translation quality in longer audio segments. ^{7, 8}

• Fine-tune with error correction and text editing models designed for Ukrainian: Models like Spivavtor enhance text editing tasks such as grammatical error correction and text coherence, supporting improved accuracy in the STT pipeline. ⁹

Overall, developing effective Ukrainian speech-to-text modules involves leveraging available pretrained models and datasets, combining hybrid architectures, applying transfer learning, integrating linguistic knowledge, and using context-aware and error correction systems tailored for Ukrainian.

If you want, further detailed guidance on specific tools, datasets, or frameworks can be provided. Would you like recommendations for open-source Ukrainian speech datasets or specific models to start with?

References

• Whisper-Slu: Extending a Pretrained Speech-to-Text Transformer for Low Resource Spoken Language Understanding

• Transcribe, Align and Segment: Creating speech datasets for low-resource languages

• A Hybrid Deep Learning Framework for Speech-to-Text Conversion as Part of Telemedicine System Integrated With 5G

• Cross-lingual Knowledge Transfer and Iterative Pseudo-labeling for Low-Resource Speech Recognition with Transducers

• ON STYLISTIC DIFFERENTIATION IN UKRAINIAN POETIC SPEECH BASED ON THE SYNTACTIC STRUCTURE OF SENTENCES

• The Grammar and Syntax Based Corpus Analysis Tool For The Ukrainian Language

• Memory-Augmented speech-to-text Translation with Multi-Scale Context Translation Strategy

• DoCIA: An Online Document-Level Context Incorporation Agent for Speech Translation

• Spivavtor: An Instruction Tuned Ukrainian Text Editing Model

• Pemanfaatan AI Speech To Text untuk Menstimulasi Kemampuan Berbicara Anak Usia Dini

• Indexing of the negative sentiment of the Ukrainian text by “TextAttributor 1.0”

• Development of a System and Interface for Speech Synthesis in Ukrainian for Websites

• EFFECTIVE TEACHING OF PHRASEOLOGY IN THE COURSE OF UKRAINIAN AS A FOREIGN LANGUAGE: PRACTICAL APPROACHES

• Linguistic_Hygenist at PAN 2024 TextDetox: HybridDetox - A Combination of Supervised and Unsupervised Methods for Effective Multilingual Text Detoxification

• From Zero to Production: Baltic-Ukrainian Machine Translation Systems to Aid Refugees

• From Zero to Production: Baltic-Ukrainian Machine Translation Systems to Aid Refugees

• From Bytes to Borsch: Fine-Tuning Gemma and Mistral for the Ukrainian Language Representation

• Benchmarking Multimodal Models for Ukrainian Language Understanding Across Academic and Cultural Domains

• IMPLEMENTING AUDIO-LINGUAL METHOD TO TEACHING UKRAINIAN AS A FOREIGN LANGUAGE AT THE INITIAL STAGE

• Handwritten Text Recognition of Ukrainian Manuscripts in the 21st Century: Possibilities, Challenges, and the Future of the First Generic AI-based Model

• Cross-lingual Text Classification Transfer: The Case of Ukrainian

• CUNI Systems for the WMT22 Czech-Ukrainian Translation Task

• ARCHITECTURE AND TRAINING ALGORITHM FOR NEURAL NETWORK TO RECOGNIZE VOICE SIGNALS

• Textless Unit-to-Unit training for Many-to-Many Multilingual Speech-to-Speech Translation

• Charles Translator: A Machine Translation System between Ukrainian and Czech

• The Ukrainian language communicative competence formation of foreign students based on the student-centered approach (in the process of teaching Ukrainian as a foreign language)