CTA-RNN: Channel and Temporal-wise Attention RNN Leveraging Pre-trained ASR Embeddings for Speech Emotion Recognition

• URL: http://arxiv.org/abs/2203.17023v1
• Date: Thu, 31 Mar 2022 13:32:51 GMT
• Title: CTA-RNN: Channel and Temporal-wise Attention RNN Leveraging Pre-trained ASR Embeddings for Speech Emotion Recognition
• Authors: Chengxin Chen, Pengyuan Zhang
• Abstract summary: We propose a novel channel and temporal-wise attention RNN architecture based on the intermediate representations of pre-trained ASR models. We evaluate our approach on two popular benchmark datasets, IEMOCAP and MSP-IMPROV.
• Score: 20.02248459288662
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Previous research has looked into ways to improve speech emotion recognition (SER) by utilizing both acoustic and linguistic cues of speech. However, the potential association between state-of-the-art ASR models and the SER task has yet to be investigated. In this paper, we propose a novel channel and temporal-wise attention RNN (CTA-RNN) architecture based on the intermediate representations of pre-trained ASR models. Specifically, the embeddings of a large-scale pre-trained end-to-end ASR encoder contain both acoustic and linguistic information, as well as the ability to generalize to different speakers, making them well suited for downstream SER task. To further exploit the embeddings from different layers of the ASR encoder, we propose a novel CTA-RNN architecture to capture the emotional salient parts of embeddings in both the channel and temporal directions. We evaluate our approach on two popular benchmark datasets, IEMOCAP and MSP-IMPROV, using both within-corpus and cross-corpus settings. Experimental results show that our proposed method can achieve excellent performance in terms of accuracy and robustness.

Related papers

• ROSE: A Recognition-Oriented Speech Enhancement Framework in Air Traffic Control Using Multi-Objective Learning [6.60571587618006]
  Radio speech echo is a specific phenomenon in the air traffic control (ATC) domain, which degrades speech quality and impacts automatic speech recognition (ASR) accuracy. In this work, a time-domain recognition-oriented speech enhancement framework is proposed to improve speech intelligibility and advance ASR accuracy. The framework serves as a plug-and-play tool in ATC scenarios and does not require additional retraining of the ASR model.
  arXiv  Detail & Related papers  (2023-12-11T04:51:41Z)
• AV-RIR: Audio-Visual Room Impulse Response Estimation [49.469389715876915]
  Accurate estimation of Room Impulse Response (RIR) is important for speech processing and AR/VR applications. We propose AV-RIR, a novel multi-modal multi-task learning approach to accurately estimate the RIR from a given reverberant speech signal and visual cues of its corresponding environment.
  arXiv  Detail & Related papers  (2023-11-30T22:58:30Z)
• Towards Improved Room Impulse Response Estimation for Speech Recognition [53.04440557465013]
  We propose a novel approach for blind room impulse response (RIR) estimation systems in the context of far-field automatic speech recognition (ASR) We first draw the connection between improved RIR estimation and improved ASR performance, as a means of evaluating neural RIR estimators. We then propose a generative adversarial network (GAN) based architecture that encodes RIR features from reverberant speech and constructs an RIR from the encoded features.
  arXiv  Detail & Related papers  (2022-11-08T00:40:27Z)
• End-to-End Active Speaker Detection [58.7097258722291]
  We propose an end-to-end training network where feature learning and contextual predictions are jointly learned. We also introduce intertemporal graph neural network (iGNN) blocks, which split the message passing according to the main sources of context in the ASD problem. Experiments show that the aggregated features from the iGNN blocks are more suitable for ASD, resulting in state-of-the art performance.
  arXiv  Detail & Related papers  (2022-03-27T08:55:28Z)
• ASR-Aware End-to-end Neural Diarization [15.172086811068962]
  We present a Conformer-based end-to-end neural diarization (EEND) model that uses both acoustic input and features derived from an automatic speech recognition (ASR) model. Three modifications to the Conformer-based EEND architecture are proposed to incorporate the features. Experiments on the two-speaker English conversations of Switchboard+SRE data sets show that multi-task learning with position-in-word information is the most effective way of utilizing ASR features.
  arXiv  Detail & Related papers  (2022-02-02T21:17:14Z)
• Improving RNN Transducer Based ASR with Auxiliary Tasks [21.60022481898402]
  End-to-end automatic speech recognition (ASR) models with a single neural network have recently demonstrated state-of-the-art results. In this work, we examine ways in which recurrent neural network transducer (RNN-T) can achieve better ASR accuracy via performing auxiliary tasks.
  arXiv  Detail & Related papers  (2020-11-05T21:46:32Z)
• Dual-mode ASR: Unify and Improve Streaming ASR with Full-context Modeling [76.43479696760996]
  We propose a unified framework, Dual-mode ASR, to train a single end-to-end ASR model with shared weights for both streaming and full-context speech recognition. We show that the latency and accuracy of streaming ASR significantly benefit from weight sharing and joint training of full-context ASR.
  arXiv  Detail & Related papers  (2020-10-12T21:12:56Z)
• Attention Driven Fusion for Multi-Modal Emotion Recognition [39.295892047505816]
  We present a deep learning-based approach to exploit and fuse text and acoustic data for emotion classification. We use a SincNet layer, based on parameterized sinc functions with band-pass filters, to extract acoustic features from raw audio followed by a DCNN. For text processing, we use two branches (a DCNN and a Bi-direction RNN followed by a DCNN) in parallel where cross attention is introduced to infer the N-gram level correlations.
  arXiv  Detail & Related papers  (2020-09-23T08:07:58Z)
• Improving Readability for Automatic Speech Recognition Transcription [50.86019112545596]
  We propose a novel NLP task called ASR post-processing for readability (APR) APR aims to transform the noisy ASR output into a readable text for humans and downstream tasks while maintaining the semantic meaning of the speaker. We compare fine-tuned models based on several open-sourced and adapted pre-trained models with the traditional pipeline method.
  arXiv  Detail & Related papers  (2020-04-09T09:26:42Z)
• Joint Contextual Modeling for ASR Correction and Language Understanding [60.230013453699975]
  We propose multi-task neural approaches to perform contextual language correction on ASR outputs jointly with language understanding (LU) We show that the error rates of off the shelf ASR and following LU systems can be reduced significantly by 14% relative with joint models trained using small amounts of in-domain data.
  arXiv  Detail & Related papers  (2020-01-28T22:09:25Z)

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.