Vocal Tract Length Warped Features for Spoken Keyword Spotting

• URL: http://arxiv.org/abs/2501.03523v1
• Date: Tue, 07 Jan 2025 04:38:28 GMT
• Title: Vocal Tract Length Warped Features for Spoken Keyword Spotting
• Authors: Achintya kr. Sarkar, Priyanka Dwivedi, Zheng-Hua Tan,
• Abstract summary: We propose several methods that incorporate vocal tract length (VTL) features for spoken keyword spotting (KWS)<n>The first method, VTL-independent KWS, involves training a single deep neural network (DNN) that utilizes VTL features with various warping factors.<n>The second method scores the conventional features of a test utterance (without VTL warping) against the DNN.<n>The third method, VTL-concatenation KWS, warped VTL features to form high-dimensional features for KWS.
• Score: 11.362295176098067
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose several methods that incorporate vocal tract length (VTL) warped features for spoken keyword spotting (KWS). The first method, VTL-independent KWS, involves training a single deep neural network (DNN) that utilizes VTL features with various warping factors. During training, a specific VTL feature is randomly selected per epoch, allowing the exploration of VTL variations. During testing, the VTL features with different warping factors of a test utterance are scored against the DNN and combined with equal weight. In the second method scores the conventional features of a test utterance (without VTL warping) against the DNN. The third method, VTL-concatenation KWS, concatenates VTL warped features to form high-dimensional features for KWS. Evaluations carried out on the English Google Command dataset demonstrate that the proposed methods improve the accuracy of KWS.

Related papers

• Test-Time Low Rank Adaptation via Confidence Maximization for Zero-Shot Generalization of Vision-Language Models [4.655740975414312]
  This paper introduces Test-Time Low-rank adaptation (TTL) as an alternative to prompt tuning for zero-shot generalizations of large-scale vision-language models (VLMs) TTL offers a test-time-efficient adaptation approach that updates the attention weights of the transformer by maximizing prediction confidence.
  arXiv  Detail & Related papers  (2024-07-22T17:59:19Z)
• Test-Time Adaptation with CLIP Reward for Zero-Shot Generalization in Vision-Language Models [76.410400238974]
  We propose TTA with feedback to rectify the model output and prevent the model from becoming blindly confident. A CLIP model is adopted as the reward model during TTA and provides feedback for the VLM. The proposed textitreinforcement learning with CLIP feedback(RLCF) framework is highly flexible and universal.
  arXiv  Detail & Related papers  (2023-05-29T11:03:59Z)
• PVT-SSD: Single-Stage 3D Object Detector with Point-Voxel Transformer [75.2251801053839]
  We present a novel Point-Voxel Transformer for single-stage 3D detection (PVT-SSD) We propose a Point-Voxel Transformer (PVT) module that obtains long-range contexts in a cheap manner from voxels. The experiments on several autonomous driving benchmarks verify the effectiveness and efficiency of the proposed method.
  arXiv  Detail & Related papers  (2023-05-11T07:37:15Z)
• SPTS v2: Single-Point Scene Text Spotting [146.98118405786445]
  New framework, SPTS v2, allows us to train high-performing text-spotting models using a single-point annotation. Tests show SPTS v2 can outperform previous state-of-the-art single-point text spotters with fewer parameters. Experiments suggest a potential preference for single-point representation in scene text spotting.
  arXiv  Detail & Related papers  (2023-01-04T14:20:14Z)
• Position-guided Text Prompt for Vision-Language Pre-training [121.15494549650548]
  We propose a novel Position-guided Text Prompt (PTP) paradigm to enhance the visual grounding ability of cross-modal models trained with Vision-Language Pre-Training. PTP reformulates the visual grounding task into a fill-in-the-blank problem given a PTP by encouraging the model to predict the objects in the given blocks or regress the blocks of a given object. PTP achieves comparable results with object-detector based methods, and much faster inference speed since PTP discards its object detector for inference while the later cannot.
  arXiv  Detail & Related papers  (2022-12-19T18:55:43Z)
• Learning Decoupling Features Through Orthogonality Regularization [55.79910376189138]
  Keywords spotting (KWS) and speaker verification (SV) are two important tasks in speech applications. We develop a two-branch deep network (KWS branch and SV branch) with the same network structure. A novel decoupling feature learning method is proposed to push up the performance of KWS and SV simultaneously.
  arXiv  Detail & Related papers  (2022-03-31T03:18:13Z)
• Data Generation Using Pass-phrase-dependent Deep Auto-encoders for Text-Dependent Speaker Verification [25.318439244029094]
  We propose a novel method that trains pass-phrase specific deep neural network (PP-DNN) based auto-encoders for creating augmented data for text-dependent speaker verification (TD-SV) Each PP-DNN auto-encoder is trained using the utterances of a particular pass-phrase available in the target enrollment set. Experiments are conducted on the RedDots challenge 2016 database for TD-SV using short utterances.
  arXiv  Detail & Related papers  (2021-02-03T14:06:29Z)
• Vocal Tract Length Perturbation for Text-Dependent Speaker Verification with Autoregressive Prediction Coding [0.0]
  We propose a vocal tract length (VTL) perturbation method for text-dependent speaker verification (TD-SV) A set of TD-SV systems are trained, one for each VTL factor, and score-level fusion is applied to make a final decision.
  arXiv  Detail & Related papers  (2020-11-25T06:11:06Z)
• Defense against Adversarial Attacks in NLP via Dirichlet Neighborhood Ensemble [163.3333439344695]
  Dirichlet Neighborhood Ensemble (DNE) is a randomized smoothing method for training a robust model to defense substitution-based attacks. DNE forms virtual sentences by sampling embedding vectors for each word in an input sentence from a convex hull spanned by the word and its synonyms, and it augments them with the training data. We demonstrate through extensive experimentation that our method consistently outperforms recently proposed defense methods by a significant margin across different network architectures and multiple data sets.
  arXiv  Detail & Related papers  (2020-06-20T18:01:16Z)
• DiscreTalk: Text-to-Speech as a Machine Translation Problem [52.33785857500754]
  This paper proposes a new end-to-end text-to-speech (E2E-TTS) model based on neural machine translation (NMT) The proposed model consists of two components; a non-autoregressive vector quantized variational autoencoder (VQ-VAE) model and an autoregressive Transformer-NMT model.
  arXiv  Detail & Related papers  (2020-05-12T02:45:09Z)

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.