Speech Training Device Using High-Pass Noise to Suppress Lombard Effect
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The challenge is to create a training device that prevents the Lombard effect during utterance training, allowing the subject to concentrate on auditory feedback without unnecessary changes in voice characteristics, while also enabling effective recognition of voice identity.
Innovation Solution
The device includes a signal analysis unit to determine formant frequencies, a conversion unit that applies a lowpass filter with specific cutoff frequencies, a noise addition unit that adds high-pass noise, and an evaluation unit to assess compensatory responses, ensuring the subject receives feedback that does not induce the Lombard effect and allows for focused attention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If feedback speech sound is generated in a noise environment, then the subject's attention can be maintained, but Lombard effect occurs causing unnecessary utterance changes
Solution Approach 1:
The patent converts the harmful Lombard effect into a beneficial training mechanism by intentionally adding high-pass noise to feedback speech sound. This controlled noise exposure trains the subject to maintain stable utterances despite adverse acoustic conditions, transforming the previously harmful Lombard response into a useful adaptation exercise that improves speech stability in noisy environments
Solution Approach 2:
The patent changes the acoustic parameters of feedback speech sound by adding high-pass noise with specific frequency characteristics. This parameter modification allows the system to control the degree of noise exposure and adjust the training intensity, enabling the subject to adapt to noisy conditions without triggering unwanted Lombard effects through gradual parameter adjustment
2Object-generated harmful factors
If feedback speech sound is generated without noise, then Lombard effect is prevented, but the subject's attention concentration is impaired
Solution Approach 1:
The patent applies local quality by adding high-pass noise specifically to certain frequency components of the feedback speech sound while maintaining other components clear. This selective noise application allows the subject to focus attention on specific frequency ranges while adapting to noise in others, preventing overall Lombard effect while maintaining attention concentration through targeted acoustic challenges
3Measurement precision
If formant frequencies are changed in feedback, then compensatory response is observed, but other utterance features may be unnecessarily altered
Solution Approach 1:
The patent segments the speech signal processing into distinct frequency bands, applying formant frequency changes to specific bands while adding high-pass noise to other bands. This segmentation allows independent control of formant manipulation and noise addition, enabling precise formant frequency control for training compensatory response while preventing unnecessary alterations in other utterance features through isolated frequency band processing
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables effective utterance training by preventing the Lombard effect and allowing the subject to concentrate on auditory feedback, while maintaining the ability to recognize voice identity and produce clear, stable utterances.
Implementation Method 1
a conversion unit that applies a lowpass filter with a cutoff frequency being a first predetermined value with or without change of feedback formant frequencies
Implementation Method 2
a noise addition unit that adds high-pass noise to the converted speech signal
Implementation Method 3
a feedback unit that feeds back the converted speech signal with the high-pass noise added to a subject
Data Source
AI summary
A training device changes feedback formant frequencies which are formant frequencies of a picked-up speech signal, applies a lowpass filter, converts the picked-up speech signal, adds high-pass noise to the converted speech signal, feeds back the converted speech signal with the high-pass noise added to a subject, calculates a compensatory response vector by using pickup formant frequencies which are formant frequencies of a speech signal acquired by picking up an utterance made by the subject while feeding back a speech signal that has been converted with change of the feedback formant frequencies to the subject, and pickup formant frequencies which are formant frequencies of a speech signal acquired by picking up an utterance made by the subject while feeding back a speech signal that has been converted without change of the feedback formant frequencies to the subject, and determines an evaluation based on the compensatory response vector and a correct compensatory response vector.