Multi-modal Automated Speech Scoring using Attention Fusion

• URL: http://arxiv.org/abs/2005.08182v2
• Date: Sun, 28 Nov 2021 08:25:48 GMT
• Title: Multi-modal Automated Speech Scoring using Attention Fusion
• Authors: Manraj Singh Grover, Yaman Kumar, Sumit Sarin, Payman Vafaee, Mika Hama, Rajiv Ratn Shah
• Abstract summary: We propose a novel multi-modal end-to-end neural approach for automated assessment of non-native English speakers' spontaneous speech. We employ Bi-directional Recurrent Convolutional Neural Networks and Bi-directional Long Short-Term Memory Neural Networks to encode acoustic and lexical cues from spectrograms and transcriptions. We find combined attention to both lexical and acoustic cues significantly improves the overall performance of the system.
• Score: 46.94442359735952
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this study, we propose a novel multi-modal end-to-end neural approach for automated assessment of non-native English speakers' spontaneous speech using attention fusion. The pipeline employs Bi-directional Recurrent Convolutional Neural Networks and Bi-directional Long Short-Term Memory Neural Networks to encode acoustic and lexical cues from spectrograms and transcriptions, respectively. Attention fusion is performed on these learned predictive features to learn complex interactions between different modalities before final scoring. We compare our model with strong baselines and find combined attention to both lexical and acoustic cues significantly improves the overall performance of the system. Further, we present a qualitative and quantitative analysis of our model.

Related papers

• EEG-Based Speech Decoding: A Novel Approach Using Multi-Kernel Ensemble Diffusion Models [0.0]
  We propose an ensemble learning framework for electroencephalogram-based overt speech classification. The ensemble comprises three models with kernel sizes of 51, 101, and 201. Results indicate that the proposed ensemble-based approach significantly outperforms individual models and existing state-of-the-art techniques.
  arXiv  Detail & Related papers  (2024-11-14T09:23:58Z)
• Improved Contextual Recognition In Automatic Speech Recognition Systems By Semantic Lattice Rescoring [4.819085609772069]
  We propose a novel approach for enhancing contextual recognition within ASR systems via semantic lattice processing. Our solution consists of using Hidden Markov Models and Gaussian Mixture Models (HMM-GMM) along with Deep Neural Networks (DNN) models for better accuracy. We demonstrate the effectiveness of our proposed framework on the LibriSpeech dataset with empirical analyses.
  arXiv  Detail & Related papers  (2023-10-14T23:16:05Z)
• Improving Audio-Visual Speech Recognition by Lip-Subword Correlation Based Visual Pre-training and Cross-Modal Fusion Encoder [58.523884148942166]
  We propose two novel techniques to improve audio-visual speech recognition (AVSR) under a pre-training and fine-tuning training framework. First, we explore the correlation between lip shapes and syllable-level subword units in Mandarin to establish good frame-level syllable boundaries from lip shapes. Next, we propose an audio-guided cross-modal fusion encoder (CMFE) neural network to utilize main training parameters for multiple cross-modal attention layers.
  arXiv  Detail & Related papers  (2023-08-14T08:19:24Z)
• Canonical Cortical Graph Neural Networks and its Application for Speech Enhancement in Future Audio-Visual Hearing Aids [0.726437825413781]
  This paper proposes a more biologically plausible self-supervised machine learning approach that combines multimodal information using intra-layer modulations together with canonical correlation analysis (CCA) The approach outperformed recent state-of-the-art results considering both better clean audio reconstruction and energy efficiency, described by a reduced and smother neuron firing rate distribution.
  arXiv  Detail & Related papers  (2022-06-06T15:20:07Z)
• Speaker Embedding-aware Neural Diarization: a Novel Framework for Overlapped Speech Diarization in the Meeting Scenario [51.5031673695118]
  We reformulate overlapped speech diarization as a single-label prediction problem. We propose the speaker embedding-aware neural diarization (SEND) system.
  arXiv  Detail & Related papers  (2022-03-18T06:40:39Z)
• Deep Learning For Prominence Detection In Children's Read Speech [13.041607703862724]
  We present a system that operates on segmented speech waveforms to learn features relevant to prominent word detection for children's oral fluency assessment. The chosen CRNN (convolutional recurrent neural network) framework, incorporating both word-level features and sequence information, is found to benefit from the perceptually motivated SincNet filters.
  arXiv  Detail & Related papers  (2021-10-27T08:51:42Z)
• Attention Bottlenecks for Multimodal Fusion [90.75885715478054]
  Machine perception models are typically modality-specific and optimised for unimodal benchmarks. We introduce a novel transformer based architecture that uses fusion' for modality fusion at multiple layers. We conduct thorough ablation studies, and achieve state-of-the-art results on multiple audio-visual classification benchmarks.
  arXiv  Detail & Related papers  (2021-06-30T22:44:12Z)
• Preliminary study on using vector quantization latent spaces for TTS/VC systems with consistent performance [55.10864476206503]
  We investigate the use of quantized vectors to model the latent linguistic embedding. By enforcing different policies over the latent spaces in the training, we are able to obtain a latent linguistic embedding. Our experiments show that the voice cloning system built with vector quantization has only a small degradation in terms of perceptive evaluations.
  arXiv  Detail & Related papers  (2021-06-25T07:51:35Z)
• Correlation based Multi-phasal models for improved imagined speech EEG recognition [22.196642357767338]
  This work aims to profit from the parallel information contained in multi-phasal EEG data recorded while speaking, imagining and performing articulatory movements corresponding to specific speech units. A bi-phase common representation learning module using neural networks is designed to model the correlation and between an analysis phase and a support phase. The proposed approach further handles the non-availability of multi-phasal data during decoding.
  arXiv  Detail & Related papers  (2020-11-04T09:39:53Z)
• Any-to-Many Voice Conversion with Location-Relative Sequence-to-Sequence Modeling [61.351967629600594]
  This paper proposes an any-to-many location-relative, sequence-to-sequence (seq2seq), non-parallel voice conversion approach. In this approach, we combine a bottle-neck feature extractor (BNE) with a seq2seq synthesis module. Objective and subjective evaluations show that the proposed any-to-many approach has superior voice conversion performance in terms of both naturalness and speaker similarity.
  arXiv  Detail & Related papers  (2020-09-06T13:01:06Z)

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.