Far-Field Acoustic Model Training Using Diffuse Reverberation
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Solution Overview
Problem
Far-field speech recognition systems face challenges due to the mismatch between training and test conditions, and existing methods for generating realistic room impulse responses are cumbersome and computationally expensive.
Innovation Solution
The method involves simulating diffuse room reverberation without simulating individual early reflections, using room impulse responses that decay over time, and applying these responses to near-field training vectors to produce simulated far-field vocalization signals, which are then used to train an acoustic model for far-field speech recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual early reflections from room surfaces are simulated, then the realism of room impulse responses is improved, but the computational complexity and time required increase significantly
Solution Approach 1:
The patent extracts only the essential reverberation characteristics from the complete room impulse response simulation process. Instead of simulating every individual early reflection, the invention extracts the diffuse reverberation tail and applies it to the dry speech signal, thereby achieving realistic room impulse responses with significantly reduced computational time.
Solution Approach 2:
The patent applies partial action by simulating only the necessary components of room acoustics. Rather than computing the full sequence of early reflections and late reverberations, the invention applies diffuse reverberation starting from a predetermined time point (e.g., 50ms or 100ms after the direct sound), providing sufficient realism for speech recognition without excessive computational effort.
2Reliability
If complete room impulse responses with individual early reflections are generated, then the accuracy of far-field speech recognition is improved, but the complexity of the training process increases
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing diffuse reverberation impulse responses for various room conditions. During the training process, these pre-computed reverberation tails are simply applied to the dry speech signals, avoiding the need to simulate complete room impulse responses with individual early reflections during each training iteration, thereby reducing training complexity while maintaining recognition accuracy.
3Measurement precision
If diffuse reverberation is simulated for the entire duration, then the realism is improved, but the computational resources required increase unnecessarily
Solution Approach 1:
The patent applies periodic action by introducing a delay period before diffuse reverberation is applied. The reverberation tail is not applied from the beginning of the signal but starts after a predetermined time interval (e.g., 50ms or 100ms), corresponding to when early reflections transition to diffuse reverberation. This periodic application reduces computational energy while maintaining realism during the critical speech recognition periods.
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 improves the efficiency of far-field speech recognition by reducing computational complexity while maintaining acceptable performance, as demonstrated by lower word error rates and increased sensitivity in audio search tasks compared to baseline systems.
Implementation Method 1
simulating diffuse room reverberation but not simulating individual early reflections from room surfaces... simulating room impulse responses corresponding to a diffuse sound that decays over time according to a reverberation time
Data Source
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AI summary
Computer-implemented methods for training an acoustic model for a far-field utterance processing system are provided. The acoustic model may be configured to map an input audio signal into linguistic or paralinguistic units. The training may involve imparting far-field acoustic characteristics upon near-field training vectors that include a plurality of near-microphone utterance signals. Imparting the far-field acoustic characteristics may involve generating a plurality of simulated room impulse responses, convolving one or more of the simulated room impulse responses with the near-field training vectors, to produce a plurality of simulated far-field utterance signals and saving the results of the training in one or more non-transitory memory devices corresponding with the acoustic model. Generating simulated room impulse responses may involve simulating room reverberation times but not simulating early reflections from room surfaces.