# Cascade Speech Translation for the Kazakh Language
## 2 Related Work  
This section presents an overview of related work in the field of cascade speech translation, as no prior research specifically focused on speech translation from Kazakh to any other language. We traced the development of cascade speech translation approaches from their early stages to the current state, providing valuable context for our research. However, it is important to note that this section does not cover the extensive research conducted on ASR and MT for the Kazakh language. Instead, we focused on the broader landscape of cascade speech translation. Additionally, this section discusses speech translation datasets used in research and examines various speech translation evaluation campaigns that have played a crucial role in assessing and advancing the performance of cascade systems. In the early stages of speech translation, particularly in the Janus project [8; 9], a simple cascading approach was utilized. This involved developing separate systems for speech recognition and translation, where the best hypothesis from the speech recognizer was passed as input to the translation system. Another initial approach involved template matching for the translation stage [10]. Researchers also explored the concept of speech-to-speech translation, which aimed to generate spoken output instead of written text [9]. However, the decision of whether to produce text or speech output was typically treated as a user interface concern that required separate evaluation. This could be accomplished by employing speech synthesis to convert the translated text into speech. These early efforts primarily focused on specific domains, such as scheduling dialog scenarios, where promising outcomes were observed. However, a major challenge arose with error propagation from the speech recognizer, particularly when using interlingua-based machine translation methods that relied on parsers expecting well-structured inputs [9; 11]. This issue was particularly significant because the prevalent interlingua-based machine translation approach heavily depended on parsers that had certain expectations. Consequently, errors introduced by the speech recognizer posed considerable difficulties. As the field of machine translation progressed, there was a notable shift towards data-driven approaches, aligning with the broader trends in general machine translation research [12]. These data-driven methods began to gain prominence as researchers recognized their potential in improving speech translation accuracy and performance. Researchers shifted their focus towards finding ways to improve the integration of the speech recognition and translation stages. A notable work, described in [13], presented a probabilistic framework for fully integrating data-driven approaches. The study demonstrated that a tight integration of ASR and MT components brings benefits to the speech translation task. One approach to achieving better integration between recognition and translation was the transition from 1-best translation to n-best translation, where multiple translation hypotheses were considered in [14; 15]. Another area of focus in making MT robust to ASR errors is through training the MT system on parallel data that include real or emulated ASR errors. By simulating or using real-world ASR errors in the training data, researchers aim to create MT models that are better equipped to manage the challenges posed by imperfect speech recognition outputs. A method for reducing the mismatch between the ASR output and the MT input was presented in [16]. Their approach involved training the translation system using automatically transcribed speech. In their experiments, they observed an improvement of up to 0.9 BLEU on the English-to-French speech translation task at the International Speech Translation Workshop 2012 (IWSLT 2012). The goal of their approach was to leverage the advantages of ASR output in improving the performance of the translation system. In [17], the authors proposed a technique to simulate errors generated by the ASR system.