Text-Aware Adapter for Few-Shot Keyword Spotting

• URL: http://arxiv.org/abs/2412.18142v1
• Date: Tue, 24 Dec 2024 03:54:40 GMT
• Title: Text-Aware Adapter for Few-Shot Keyword Spotting
• Authors: Youngmoon Jung, Jinyoung Lee, Seungjin Lee, Myunghun Jung, Yong-Hyeok Lee, Hoon-Young Cho,
• Abstract summary: We propose a novel few-shot transfer learning method, called text-aware adapter (TA-adapter) In our experiments, the TA-adapter demonstrated significant performance improvements across 35 distinct keywords from the Google Speech Commands V2 dataset.
• Score: 13.040457187781671
• License:
• Abstract: Recent advances in flexible keyword spotting (KWS) with text enrollment allow users to personalize keywords without uttering them during enrollment. However, there is still room for improvement in target keyword performance. In this work, we propose a novel few-shot transfer learning method, called text-aware adapter (TA-adapter), designed to enhance a pre-trained flexible KWS model for specific keywords with limited speech samples. To adapt the acoustic encoder, we leverage a jointly pre-trained text encoder to generate a text embedding that acts as a representative vector for the keyword. By fine-tuning only a small portion of the network while keeping the core components' weights intact, the TA-adapter proves highly efficient for few-shot KWS, enabling a seamless return to the original pre-trained model. In our experiments, the TA-adapter demonstrated significant performance improvements across 35 distinct keywords from the Google Speech Commands V2 dataset, with only a 0.14% increase in the total number of parameters.

Related papers

• Retrieval is Accurate Generation [99.24267226311157]
  We introduce a novel method that selects context-aware phrases from a collection of supporting documents. Our model achieves the best performance and the lowest latency among several retrieval-augmented baselines.
  arXiv  Detail & Related papers  (2024-02-27T14:16:19Z)
• Open-vocabulary Keyword-spotting with Adaptive Instance Normalization [18.250276540068047]
  We propose AdaKWS, a novel method for keyword spotting in which a text encoder is trained to output keyword-conditioned normalization parameters. We show significant improvements over recent keyword spotting and ASR baselines.
  arXiv  Detail & Related papers  (2023-09-13T13:49:42Z)
• Evolutionary Verbalizer Search for Prompt-based Few Shot Text Classification [5.583948835737293]
  We propose a novel evolutionary verbalizer search (EVS) algorithm to improve prompt-based tuning with the high-performance verbalizer. In this paper, we focus on automatically constructing the optimal verbalizer and propose a novelEVS algorithm to improve prompt-based tuning with the high-performance verbalizer.
  arXiv  Detail & Related papers  (2023-06-18T10:03:11Z)
• Three ways to improve feature alignment for open vocabulary detection [88.65076922242184]
  Key problem in zero-shot open vocabulary detection is how to align visual and text features, so that the detector performs well on unseen classes. Previous approaches train the feature pyramid and detection head from scratch, which breaks the vision-text feature alignment established during pretraining. We propose three methods to alleviate these issues. Firstly, a simple scheme is used to augment the text embeddings which prevents overfitting to a small number of classes seen during training. Secondly, the feature pyramid network and the detection head are modified to include trainable shortcuts. Finally, a self-training approach is used to leverage a larger corpus of
  arXiv  Detail & Related papers  (2023-03-23T17:59:53Z)
• PatternRank: Leveraging Pretrained Language Models and Part of Speech for Unsupervised Keyphrase Extraction [0.6767885381740952]
  We present PatternRank, which pretrained language models and part-of-speech for unsupervised keyphrase extraction from single documents. Our experiments show PatternRank achieves higher precision, recall and F1-scores than previous state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-10-11T08:23:54Z)
• Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages [58.43299730989809]
  We introduce Wav2Seq, the first self-supervised approach to pre-train both parts of encoder-decoder models for speech data. We induce a pseudo language as a compact discrete representation, and formulate a self-supervised pseudo speech recognition task. This process stands on its own, or can be applied as low-cost second-stage pre-training.
  arXiv  Detail & Related papers  (2022-05-02T17:59:02Z)
• Vision-Language Pre-Training for Boosting Scene Text Detectors [57.08046351495244]
  We specifically adapt vision-language joint learning for scene text detection. We propose to learn contextualized, joint representations through vision-language pre-training. The pre-trained model is able to produce more informative representations with richer semantics.
  arXiv  Detail & Related papers  (2022-04-29T03:53:54Z)
• Learning Rich Representation of Keyphrases from Text [12.698835743464313]
  We show how to learn task-specific language models aimed towards learning rich representation of keyphrases from text documents. In the discriminative setting, we introduce a new pre-training objective - Keyphrase Boundary Infilling with Replacement (KBIR) In the generative setting, we introduce a new pre-training setup for BART - KeyBART, that reproduces the keyphrases related to the input text in the CatSeq format.
  arXiv  Detail & Related papers  (2021-12-16T01:09:51Z)
• A study on the efficacy of model pre-training in developing neural text-to-speech system [55.947807261757056]
  This study aims to understand better why and how model pre-training can positively contribute to TTS system performance. It is found that the TTS system could achieve comparable performance when the pre-training data is reduced to 1/8 of its original size.
  arXiv  Detail & Related papers  (2021-10-08T02:09:28Z)
• Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation [56.830395467247016]
  We propose a model of semantic memory for WSD in a meta-learning setting. Our model is based on hierarchical variational inference and incorporates an adaptive memory update rule via a hypernetwork. We show our model advances the state of the art in few-shot WSD, supports effective learning in extremely data scarce scenarios.
  arXiv  Detail & Related papers  (2021-06-05T20:40:01Z)
• Teaching keyword spotters to spot new keywords with limited examples [6.251896411370577]
  We present KeySEM, a speech embedding model pre-trained on the task of recognizing a large number of keywords. KeySEM is well suited to on-device environments where post-deployment learning and ease of customization are often desirable.
  arXiv  Detail & Related papers  (2021-06-04T12:43:36Z)

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.