DeepSpeech models show Human-like Performance and Processing of Cochlear Implant Inputs

• URL: http://arxiv.org/abs/2407.20535v1
• Date: Tue, 30 Jul 2024 04:32:27 GMT
• Title: DeepSpeech models show Human-like Performance and Processing of Cochlear Implant Inputs
• Authors: Cynthia R. Steinhardt, Menoua Keshishian, Nima Mesgarani, Kim Stachenfeld,
• Abstract summary: We use the deep neural network (DNN) DeepSpeech2 as a paradigm to investigate how natural input and cochlear implant-based inputs are processed over time. We generate naturalistic and cochlear implant-like inputs from spoken sentences and test the similarity of model performance to human performance. We find that dynamics over time in each layer are affected by context as well as input type.
• Score: 12.234206036041218
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Cochlear implants(CIs) are arguably the most successful neural implant, having restored hearing to over one million people worldwide. While CI research has focused on modeling the cochlear activations in response to low-level acoustic features, we hypothesize that the success of these implants is due in large part to the role of the upstream network in extracting useful features from a degraded signal and learned statistics of language to resolve the signal. In this work, we use the deep neural network (DNN) DeepSpeech2, as a paradigm to investigate how natural input and cochlear implant-based inputs are processed over time. We generate naturalistic and cochlear implant-like inputs from spoken sentences and test the similarity of model performance to human performance on analogous phoneme recognition tests. Our model reproduces error patterns in reaction time and phoneme confusion patterns under noise conditions in normal hearing and CI participant studies. We then use interpretability techniques to determine where and when confusions arise when processing naturalistic and CI-like inputs. We find that dynamics over time in each layer are affected by context as well as input type. Dynamics of all phonemes diverge during confusion and comprehension within the same time window, which is temporally shifted backward in each layer of the network. There is a modulation of this signal during processing of CI which resembles changes in human EEG signals in the auditory stream. This reduction likely relates to the reduction of encoded phoneme identity. These findings suggest that we have a viable model in which to explore the loss of speech-related information in time and that we can use it to find population-level encoding signals to target when optimizing cochlear implant inputs to improve encoding of essential speech-related information and improve perception.

Related papers

• Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks [59.38765771221084]
  We present a physiologically inspired speech recognition architecture compatible and scalable with deep learning frameworks. We show end-to-end gradient descent training leads to the emergence of neural oscillations in the central spiking neural network. Our findings highlight the crucial inhibitory role of feedback mechanisms, such as spike frequency adaptation and recurrent connections, in regulating and synchronising neural activity to improve recognition performance.
  arXiv  Detail & Related papers  (2024-04-22T09:40:07Z)
• Artificial Intelligence for Cochlear Implants: Review of Strategies, Challenges, and Perspectives [2.608119698700597]
  This review aims to comprehensively cover advancements in CI-based ASR and speech enhancement, among other related aspects. The review will delve into potential applications and suggest future directions to bridge existing research gaps in this domain.
  arXiv  Detail & Related papers  (2024-03-17T11:28:23Z)
• Surrogate Gradient Spiking Neural Networks as Encoders for Large Vocabulary Continuous Speech Recognition [91.39701446828144]
  We show that spiking neural networks can be trained like standard recurrent neural networks using the surrogate gradient method. They have shown promising results on speech command recognition tasks. In contrast to their recurrent non-spiking counterparts, they show robustness to exploding gradient problems without the need to use gates.
  arXiv  Detail & Related papers  (2022-12-01T12:36:26Z)
• End-to-End Binaural Speech Synthesis [71.1869877389535]
  We present an end-to-end speech synthesis system that combines a low-bitrate audio system with a powerful decoder. We demonstrate the capability of the adversarial loss in capturing environment effects needed to create an authentic auditory scene.
  arXiv  Detail & Related papers  (2022-07-08T05:18:36Z)
• 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)
• Self-supervised models of audio effectively explain human cortical responses to speech [71.57870452667369]
  We capitalize on the progress of self-supervised speech representation learning to create new state-of-the-art models of the human auditory system. We show that these results show that self-supervised models effectively capture the hierarchy of information relevant to different stages of speech processing in human cortex.
  arXiv  Detail & Related papers  (2022-05-27T22:04:02Z)
• 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)
• Multi-Modal Detection of Alzheimer's Disease from Speech and Text [3.702631194466718]
  We propose a deep learning method that utilizes speech and the corresponding transcript simultaneously to detect Alzheimer's disease (AD) The proposed method achieves 85.3% 10-fold cross-validation accuracy when trained and evaluated on the Dementiabank Pitt corpus.
  arXiv  Detail & Related papers  (2020-11-30T21:18:17Z)
• Multi-Tones' Phase Coding (MTPC) of Interaural Time Difference by Spiking Neural Network [68.43026108936029]
  We propose a pure spiking neural network (SNN) based computational model for precise sound localization in the noisy real-world environment. We implement this algorithm in a real-time robotic system with a microphone array. The experiment results show a mean error azimuth of 13 degrees, which surpasses the accuracy of the other biologically plausible neuromorphic approach for sound source localization.
  arXiv  Detail & Related papers  (2020-07-07T08:22:56Z)
• A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applications [11.086440815804226]
  We present a hybrid approach where convolutional neural networks are combined with computational neuroscience to yield a real-time end-to-end model for human cochlear mechanics. The CoNNear model accurately simulates human cochlear frequency selectivity and its dependence on sound intensity. These unique CoNNear features will enable the next generation of human-like machine-hearing applications.
  arXiv  Detail & Related papers  (2020-04-30T14:43:03Z)
• Fast frequency discrimination and phoneme recognition using a biomimetic membrane coupled to a neural network [2.314552275307609]
  In the human ear, the basilar membrane plays a central role in sound recognition. Inspired by this structure, we designed and fabricated an artificial membrane that produces a spatial displacement pattern in response to an audible signal. When trained with single frequency tones, this system can unambiguously distinguish tones closely spaced in frequency.
  arXiv  Detail & Related papers  (2020-04-09T10:07:12Z)

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.