Acoustic Inverse Filter for Far-Field Speech Recognition
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Solution Overview
Problem
Existing speech recognition techniques are limited by their requirement for near-field speech input, as far-field speech is often distorted by room acoustics, making it unusable for processing.
Innovation Solution
A system that calibrates acoustic interfaces to determine and invert transformation effects within an acoustic space, allowing for accurate speech recognition from far-field sources by modeling transformations as filters and applying inverse transformations to improve signal quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a microphone is placed far from the user (far-field), then hands-free operation is enabled, but speech is distorted by room acoustics making it unusable
Solution Approach 1:
The system performs preliminary calibration by playing test tones through speakers and recording them with the microphone before actual speech recognition. This pre-characterization of the acoustic environment allows the system to pre-compute inverse filters that will compensate for room acoustics during actual use, enabling reliable far-field speech recognition without requiring the user to be close to the microphone
Solution Approach 2:
The patent introduces an acoustic model and inverse filter as an intermediary between the distorted far-field speech signal and the speech recognition system. The inverse filter acts as a signal processing mediator that reverses the acoustic transformations, converting the distorted microphone signal back into a form suitable for recognition, thus enabling hands-free operation at a distance
2Reliability
If a microphone is placed close to the user (near-field), then speech recognition accuracy is improved, but hands-free operation becomes difficult
Solution Approach 1:
The patent replaces the mechanical solution of placing the microphone physically close to the user with a signal processing solution. Instead of relying on proximity for signal quality, the system uses acoustic modeling and inverse filtering to digitally restore speech quality from far-field recordings, substituting mechanical positioning with computational processing
3Ease of operation
If room acoustics are present, then hands-free far-field operation is possible, but speech signal is transformed and distorted
Solution Approach 1:
The system turns the harmful effect of room acoustics into a benefit by characterizing the acoustic environment through calibration. The same acoustic transformations that distort speech are measured and stored as a model, then inverted to recover the original speech signal. The harmful acoustic distortion becomes useful calibration data that enables compensation
Solution Approach 2:
The calibration process performs preliminary measurement of the acoustic environment by playing test tones and recording their transformed versions. This advance characterization allows the system to pre-compute the inverse of the acoustic transformation, which is then applied during actual speech recognition to recover undistorted speech from far-field recordings
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
Enables effective far-field voice recognition by accurately compensating for acoustic distortions, allowing users to interact with devices hands-free from various distances and locations within a room.
Implementation Method 1
identify a transformation related to a position within an acoustic space
Implementation Method 2
the effects of room acoustics may transform or distort the speech
Data Source
AI summary
Embodiments of systems and methods are described for inverting transformations of signals due to room acoustics. In some implementations, a transformation of a calibration signal from a particular location in a room may be determined. From this transformation, an inverse transformation may be determined and the inverse transformation may be applied to a speech signal received from a similar location.