Large language models (LLMs) have achieved remarkable success in high-resource languages, yet their performance on Traditional Mongolian remains highly limited. A primary bottleneck is the absence of a systematic evaluation framework, which precludes quantitative comparison and obscures directions for model optimization. In this paper, we introduce TM-Bench, the first comprehensive benchmark for LLMs on Traditional Mongolian. TM-Bench adopts a hybrid construction strategy consisting of human-verified Translation-based Adaptation, Expert-Original Authoring, and Semi-automated Synthesis. It comprises 18,357 instances spanning five tasks across both natural language understanding and generation to evaluate models' reasoning, knowledge application, and linguistic proficiency. We conduct systematic evaluations across representative model families. The results show that on understanding tasks, model performance lags significantly behind high-resource languages, with only a few models performing slightly above the random baseline. For generation tasks, both automatic metrics and double-blind human evaluations reveal severe semantic collapse, failing to generate coherent text and often producing unreadable gibberish. These findings underscore the critical role of TM-Bench as a foundational infrastructure for evaluating LLMs in Traditional Mongolian and catalyzing future model optimization. Our benchmark and code are available at https://github.com/gao1948083886/TM-Bench.

Utilizing pseudo speech-label pairs synthesized via Text-to-Speech (TTS) systems as supplementary training data for automatic speech recognition (ASR) has shown significant benefits. However, the mismatch between synthesized and real speech make them unsuitable for direct use in zero-shot speech recognition tasks. In this paper, we propose a generative-adversarial model with a style truncation strategy that enhances mel-spectrograms by projecting style vectors into high-density regions. We also introduce a Contextual alignment loss function to align synthesized and real mel-spectrograms by computing global distances between high-dimensional features. Experimental results on zero-shot ASR tasks using the SLURP and LibriSpeech datasets show that our method achieves WER reductions of 4.3%, 11.1% (clean), and 6.9% (other) compared to the baseline mel-spectrograms enhanced model.

Mongolian Optical Character Recognition (OCR) systems are required for printed document digitization and Mongolian cultural resources utilization. Existing Mongolian OCR systems are based on segmentation. But, the Mongolian segmentation is more difficult than other languages. So, these methods are highly costly and error suffering. In this study, a segmentation-free based traditional Mongolian word recognition method is proposed. Specifically, we formalize the OCR task as a sequence to sequence mapping problem, in which the input Mongolian word image and the output textual string are treated as a sequence of image frames and a sequence of letters, respectively. A sequence to sequence with attention model is adopted to solve this problem. Experimental results on a dataset show the effectiveness of the proposed method.