The role of cue enhancement and frequency fine-tuning in hearing impaired phone recognition

08/09/2019
by   Ali Abavisani, et al.
0

A speech-based hearing test is designed to identify the susceptible error-prone phones for individual hearing impaired (HI) ear. Only robust tokens in the experiment noise levels had been chosen for the test. The noise-robustness of tokens is measured as SNR90 of the token, which is the signal to the speech-weighted noise ratio where a normal hearing (NH) listener would recognize the token with an accuracy of 90 tokens T1 and T2 having the same consonant-vowels but different talkers with distinct SNR90 had been presented with flat gain at listeners' most comfortable level. We studied the effects of frequency fine-tuning of the primary cue by presenting tokens of the same consonant but different vowels with similar SNR90. Additionally, we investigated the role of changing the intensity of primary cue in HI phone recognition, by presenting tokens from both sets T1 and T2. On average, 92 same CV but with a more robust talker. Additionally, using CVs with similar SNR90, on average, tokens are improved by 75 replaced vowels /A, ae, I, E/, respectively. The confusion pattern in each case provides insight into how these changes affect the phone recognition in each HI ear. We propose to prescribe hearing aid amplification tailored to individual HI ears, based on the confusion pattern, the response from cue enhancement, and the response from frequency fine-tuning of the cue.

READ FULL TEXT
research
04/04/2021

Phoneme Recognition through Fine Tuning of Phonetic Representations: a Case Study on Luhya Language Varieties

Models pre-trained on multiple languages have shown significant promise ...
research
05/27/2023

Zero-TPrune: Zero-Shot Token Pruning through Leveraging of the Attention Graph in Pre-Trained Transformers

Deployment of Transformer models on the edge is increasingly challenging...
research
10/09/2020

Token-level Adaptive Training for Neural Machine Translation

There exists a token imbalance phenomenon in natural language as differe...
research
02/28/2023

Weighted Sampling for Masked Language Modeling

Masked Language Modeling (MLM) is widely used to pretrain language model...
research
03/29/2022

Fine-tuning Image Transformers using Learnable Memory

In this paper we propose augmenting Vision Transformer models with learn...
research
09/18/2023

Are Soft Prompts Good Zero-shot Learners for Speech Recognition?

Large self-supervised pre-trained speech models require computationally ...
research
05/03/2021

Quantifying and Maximizing the Benefits of Back-End Noise Adaption on Attention-Based Speech Recognition Models

This work analyzes how attention-based Bidirectional Long Short-Term Mem...

Please sign up or login with your details

Forgot password? Click here to reset