Speech-to-Text (STT) or Automatic Speech Recognition (ASR) is a fundamental technology that converts spoken language into written text, facilitating human-machine interaction and enabling the processing and analysis of audio data in digital environments. Recent advancements in Large Language Models (LLM) have led to significant improvements in the capabilities of STT systems.

Evolution of Speech-to-Text Technology

ASR technology has a long history, with traditional approaches using statistical methods like Hidden Markov Models (HMM). The rise of deep learning and sequence-to-sequence models such as Transformer have enabled the development of "End-to-End" (E2E) systems, which simplify the process and often improve performance by directly converting audio input to text output. A significant advancement in this field has been Self-Supervised Learning (SSL), popularized by models like wav2vec 2.0; this method allows models to learn the fundamental properties of speech using large amounts of unlabeled audio data, and models pre-trained in this way can achieve high accuracy when fine-tuned with small amounts of labeled data.

Prominent Open-Source LLM-Based STT Systems

With the integration of LLMs into speech recognition, many powerful open-source systems have been developed; the best known among them is Whisper, developed by OpenAI. Whisper demonstrates high robustness against different languages, accents, and noisy conditions because it is trained with large-scale and diverse "weakly supervised" data, and its multilingual structure, various sizes, and open-source license have made it widespread; it is possible to run it efficiently on various platforms with projects like whisper.cpp. While Meta AI's projects like Seamless Communication combine multiple tasks into a single model, the MMS project focuses on low-resource languages, and toolkits like NVIDIA NeMo offer resources for model training and deployment with advanced architectures like Conformer; these models are often trained by combining different datasets.

Speech-to-Text Efforts in Turkish

The agglutinative structure and rich morphology of Turkish create unique challenges for STT systems, such as out-of-vocabulary words and segmentation, while the limited amount of publicly available labeled data compared to English often directs efforts towards adapting pre-trained multilingual models to Turkish. Whisper's multilingual capabilities have made it a popular base model for Turkish, and community-fine-tuned versions using datasets like Common Voice (e.g., sgangireddy/whisper-medium-tr) have been shared on platforms like Hugging Face; these models generally perform better in Turkish than the base model. Comparative analyses have shown that, especially in recognizing Turkish speech from different domains, fine-tuned Whisper-based models can be more successful compared to previous-generation SSL models like XLS-R.

Training Methods and Data Sources

In the "Pre-training and Fine-tuning" approach dominant in the development of current LLM-based STT systems, models first learn general speech patterns on very large amounts of often unlabeled audio data (pre-training), and then they are optimized using smaller, labeled datasets for the target language or task (fine-tuning). While open-licensed, crowd-sourced datasets like Common Voice play an important role in the fine-tuning and evaluation of Turkish STT models, the massive datasets used for pre-training large models are usually compiled from web sources and may not always be publicly available.

Challenges in Fine-Tuning Large Models

Adapting large pre-trained models to specific tasks brings technical challenges such as the requirement for sufficient and high-quality labeled data for the target, catastrophic forgetting (the risk of the model partially losing the general capabilities gained during pre-training while adapting to a new task), the risk of hallucination (the tendency to generate text not present in the input), the cost requiring significant computational resources (GPU, time), and the mismatch between the data distribution the model was trained on and the domain it is applied to; strategies such as parameter-efficient fine-tuning (PEFT) methods, data augmentation techniques, and improvements in model architecture are used to address these challenges.

Application Areas

LLM-based STT technology is currently used in a wide range of applications such as transcribing meetings, lectures, interviews, and media content, voice command systems and virtual assistants, call center conversation analysis, dictation-based text input (especially in medicine and law), automatic subtitle generation for media content, pronunciation evaluation in language learning platforms, and accessibility technologies (e.g., for the hearing impaired); the availability of open-source models enables institutions and developers to create STT solutions tailored to their specific needs while maintaining data privacy.