{"ID":2834910,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.02074","arxiv_id":"2512.02074","title":"Dialect Identification Using Resource-Efficient Fine-Tuning Approaches","abstract":"Dialect Identification (DI) is a task to recognize different dialects within the same language from a speech signal. DI can help to improve the downstream speech related tasks even when speakers have a strong dialect. However, fine-tuning a speech model for tasks like DI is expensive in terms of computation cost and memory requirement. Recent studies have explored fine-tuning pre-trained speech models for tasks like DI using Parameter-Efficient Fine-Tuning (PEFT) methods, which offer parameter efficiency but limited improvement in memory efficiency and training speed. To address these challenges, we explore Memory-Efficient Fine-Tuning (MEFT) methods, originally proposed for language processing, and apply them to the general-purpose pre-trained speech model. We then comprehensively analyze the GPU memory usage and fine-tuning speed based on various MEFT methods. As a case study, we fine-tune the Whisper model to identify six Mandarin subdialects from the KeSpeech dataset, reducing GPU memory usage by up to 73.25% and accelerating training speed by a factor of 2.1, while maintaining accuracy comparable to vanilla fine-tuning and PEFT methods.","short_abstract":"Dialect Identification (DI) is a task to recognize different dialects within the same language from a speech signal. DI can help to improve the downstream speech related tasks even when speakers have a strong dialect. However, fine-tuning a speech model for tasks like DI is expensive in terms of computation cost and me...","url_abs":"https://arxiv.org/abs/2512.02074","url_pdf":"https://arxiv.org/pdf/2512.02074v1","authors":"[\"Zirui Lin\",\"Haris Gulzar\",\"Monnika Roslianna Busto\",\"Akiko Masaki\",\"Takeharu Eda\",\"Kazuhiro Nakadai\"]","published":"2025-11-30T14:40:27Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.SD\"]","methods":"[]","has_code":false}