Efficient Adaptation of Multilingual Models for Japanese ASR

• URL: http://arxiv.org/abs/2412.10705v1
• Date: Sat, 14 Dec 2024 06:32:16 GMT
• Title: Efficient Adaptation of Multilingual Models for Japanese ASR
• Authors: Mark Bajo, Haruka Fukukawa, Ryuji Morita, Yuma Ogasawara,
• Abstract summary: This study explores fine-tuning multilingual ASR (Automatic Speech Recognition) models, specifically OpenAI's Whisper-Tiny, to improve performance in Japanese. Using Japanese-specific datasets and Low-Rank Adaptation (LoRA) along with end-to-end (E2E) training, we fine-tuned Whisper-Tiny to bridge this gap. Our results show that fine-tuning reduced Whisper-Tiny's Character Error Rate (CER) from 32.7 to 20.8 with LoRA and to 14.7 with end-to-end fine-tuning, surpassing Whisper-Base's CER of 20.
• Score: 0.0
• License:
• Abstract: This study explores fine-tuning multilingual ASR (Automatic Speech Recognition) models, specifically OpenAI's Whisper-Tiny, to improve performance in Japanese. While multilingual models like Whisper offer versatility, they often lack precision in specific languages. Conversely, monolingual models like ReazonSpeech excel in language-specific tasks but are less adaptable. Using Japanese-specific datasets and Low-Rank Adaptation (LoRA) along with end-to-end (E2E) training, we fine-tuned Whisper-Tiny to bridge this gap. Our results show that fine-tuning reduced Whisper-Tiny's Character Error Rate (CER) from 32.7 to 20.8 with LoRA and to 14.7 with end-to-end fine-tuning, surpassing Whisper-Base's CER of 20.2. However, challenges with domain-specific terms remain, highlighting the need for specialized datasets. These findings demonstrate that fine-tuning multilingual models can achieve strong language-specific performance while retaining their flexibility. This approach provides a scalable solution for improving ASR in resource-constrained environments and languages with complex writing systems like Japanese.

Related papers

• Enhancing Multilingual ASR for Unseen Languages via Language Embedding Modeling [50.62091603179394]
  Whisper, one of the most advanced ASR models, handles 99 languages effectively. However, Whisper struggles with unseen languages, those not included in its pre-training. We propose methods that exploit these relationships to enhance ASR performance on unseen languages.
  arXiv  Detail & Related papers  (2024-12-21T04:05:43Z)
• Whisper Finetuning on Nepali Language [0.0]
  This research focuses on making an exhaustive and generalized dataset followed by fine-tuning OpenAI's Whisper models to improve transcription accuracy for the Nepali language. We leverage publicly available ASR datasets and self-recorded custom datasets with a diverse range of accents, dialects, and speaking styles further enriched through augmentation. Our approach outperforms Whisper's baseline models trained on Fleur's dataset, achieving WER reductions of up to 36.2% on the small and 23.8% on medium models.
  arXiv  Detail & Related papers  (2024-11-19T15:55:56Z)
• Improving Multilingual ASR in the Wild Using Simple N-best Re-ranking [68.77659513993507]
  We present a simple and effective N-best re-ranking approach to improve multilingual ASR accuracy. Our results show spoken language identification accuracy improvements of 8.7% and 6.1%, respectively, and word error rates which are 3.3% and 2.0% lower on these benchmarks.
  arXiv  Detail & Related papers  (2024-09-27T03:31:32Z)
• An Initial Investigation of Language Adaptation for TTS Systems under Low-resource Scenarios [76.11409260727459]
  This paper explores the language adaptation capability of ZMM-TTS, a recent SSL-based multilingual TTS system. We demonstrate that the similarity in phonetics between the pre-training and target languages, as well as the language category, affects the target language's adaptation performance.
  arXiv  Detail & Related papers  (2024-06-13T08:16:52Z)
• Efficient Compression of Multitask Multilingual Speech Models [0.0]
  DistilWhisper is able to bridge the performance gap in ASR for these languages while retaining the advantages of multitask and multilingual capabilities. Our approach involves two key strategies: lightweight modular ASR fine-tuning of whisper-small using language-specific experts, and knowledge distillation from whisper-large-v2.
  arXiv  Detail & Related papers  (2024-05-02T03:11:59Z)
• Efficient Adapter Finetuning for Tail Languages in Streaming Multilingual ASR [44.949146169903074]
  The heterogeneous nature and imbalanced data abundance of different languages may cause performance degradation. Our proposed method brings 12.2% word error rate reduction on average and up to 37.5% on a single locale.
  arXiv  Detail & Related papers  (2024-01-17T06:01:16Z)
• Multilingual DistilWhisper: Efficient Distillation of Multi-task Speech Models via Language-Specific Experts [14.999359332108767]
  We propose DistilWhisper to bridge the performance gap in ASR for under-represented languages. Our approach involves two key strategies: lightweight modular ASR fine-tuning of whisper-small using language-specific experts, and knowledge distillation from whisper-large-v2. Results demonstrate that our approach is more effective than standard fine-tuning or LoRA adapters.
  arXiv  Detail & Related papers  (2023-11-02T08:37:30Z)
• Few-shot Learning with Multilingual Language Models [66.49496434282564]
  We train multilingual autoregressive language models on a balanced corpus covering a diverse set of languages. Our largest model sets new state of the art in few-shot learning in more than 20 representative languages. We present a detailed analysis of where the model succeeds and fails, showing in particular that it enables cross-lingual in-context learning.
  arXiv  Detail & Related papers  (2021-12-20T16:52:35Z)
• Adapt-and-Adjust: Overcoming the Long-Tail Problem of Multilingual Speech Recognition [58.849768879796905]
  We propose Adapt-and-Adjust (A2), a transformer-based multi-task learning framework for end-to-end multilingual speech recognition. The A2 framework overcomes the long-tail problem via three techniques: (1) exploiting a pretrained multilingual language model (mBERT) to improve the performance of low-resource languages; (2) proposing dual adapters consisting of both language-specific and language-agnostic adaptation with minimal additional parameters; and (3) overcoming the class imbalance, either by imposing class priors in the loss during training or adjusting the logits of the softmax output during inference.
  arXiv  Detail & Related papers  (2020-12-03T03:46:16Z)
• How Phonotactics Affect Multilingual and Zero-shot ASR Performance [74.70048598292583]
  A Transformer encoder-decoder model has been shown to leverage multilingual data well in IPA transcriptions of languages presented during training. We replace the encoder-decoder with a hybrid ASR system consisting of a separate AM and LM. We show that the gain from modeling crosslingual phonotactics is limited, and imposing a too strong model can hurt the zero-shot transfer.
  arXiv  Detail & Related papers  (2020-10-22T23:07:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.