# Cascade Speech Translation for the Kazakh Language
## 2 Related Work
They utilized the ASR system's pronunciation dictionary and language model to convert lexical entries into phoneme sequences. These sequences were then used to create a phoneme-to-word MT system that introduced errors in clean text to mimic ASR outputs. By training a speech translation system on synthetic ASR data generated through this technique, the authors observed consistent improvements in translation quality for English-French lectures. Another work [18] introduced a training architecture that focuses on enhancing the robustness of NMT models against speech recognition errors. Their approach addressed both the encoder and decoder components through adversarial learning and data augmentation techniques, respectively. By simultaneously addressing these components, their method aimed to bridge the gap between the output of ASR and the input of MT. Experimental results on the IWSLT2018 speech translation task demonstrated that their approach outperformed the baseline by up to 2.83 BLEU points when dealing with noisy ASR output while maintaining comparable performance on clean text. Methods to adapt a robust NMT system that can effectively manage common ASR errors were investigated [19]. In their study, they focused on the application scenarios where transcripts can be post-edited by human experts. To address this, they proposed adaptation strategies to train a single system capable of translating both clean and noisy input without supervision on the input type. This approach aimed to enhance the versatility and adaptability of the NMT system, enabling it to manage different types of input effectively. A pipeline system specifically designed for the spoken language translation (SLT) task of the IWSLT19 evaluation campaign was developed in [20]. They performed speech recognition experiments using two distinct end-to-end architectures. By combining these two architecture models into an ensemble, they were able to achieve the best results on the development sets of SLT sub-tasks. In terms of machine translation, they employed a transformer-based multilingual model, which allowed them to generate translations for all sub-tasks using just a single model. The integration of these components enabled [PERSON] et al. to create an effective and efficient system for speech translation in the IWSLT19 evaluation campaign. A cascaded approach that combined high-quality hybrid ASR with transformer-based NMT was proposed in [21]. This cascaded approach aimed to integrate accurate speech recognition provided by the ASR system with the powerful translation capabilities of the NMT system. By combining these two components, researchers aimed to enhance the performance of simultaneous speech translation systems in the IWSLT 2020 evaluation. In their study, the authors of [22] conducted a systematic comparison between state-of-the-art systems representing two paradigms in speech translation: cascade and direct. The study focused on three language directions: English to German, Italian and Spanish. To evaluate the performance of these systems, both automatic and manual evaluations were conducted. The evaluations utilized high-quality professional post-edits and annotations, ensuring accurate and reliable assessments. By systematically comparing the performance of cascade and direct systems across multiple language directions, the study provided valuable insights into the strengths and weaknesses of each approach, contributing to a deeper understanding of speech translation techniques. A comparison was made between state-of-the-art cascade and direct approaches for speech translation in [23]. The focus of the study was the under-resourced language pair of Basque-Spanish, which presents challenging linguistic phenomena, including significant differences in morphology and word order. This case study was significant as it provided insights into speech translation beyond the commonly studied English language.